Advances in Modern Tourism Research: Economic Perspectives

Advances in Modern Tourism Research

Álvaro Matias · Peter Nijkamp Paulo Neto (Eds.)

Advances in Modern Tourism Research Economic Perspectives

With 26 Figures and 65 Tables

Prof. Dr. Álvaro Matias University of Évora Department of Economics Urban and Regional Analysis and Research Laboratory Largo dos Colegiais 2 Apartado 94 7002-554 Évora Portugal [email protected]

Prof. Dr. Paulo Neto University of Évora Department of Economics Urban and Regional Analysis and Research Laboratory Largo dos Colegiais 2 Apartado 94 7000-554 Évora Portugal [email protected]

Prof. Dr. Peter Nijkamp Free University Department of Economics De Boelelaan 1105 1081 HV Amsterdam The Netherlands [email protected] Publication sponsored by:

Library of Congress Control Number: 2007923598 ISBN 978-3-7908-1717-1 Physica-Verlag Heidelberg New York This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Physica-Verlag. Violations are liable for prosecution under the German Copyright Law. Physica-Verlag is a part of Springer Science+Business Media springer.com c Physica-Verlag Heidelberg 2007 

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Editorial Preface

Many trips by car or train are nowadays leisure trips. And most passengers on board of an airplane are tourists. Indeed, we enter an age of large-scale tourism, for the time being in the industrialized part of the world, but soon to be followed by mass tourism trends in emerging economies. Consequently, tourism will become a major factor of economic growth in the future. In the past decades, we have witnessed an increasing volume of publications on tourist-economic issues, sometimes of a case study nature, sometimes of a statistically-modelling nature and sometimes of a conceptual or methodological nature. It turns out that tourism research is on a rising edge, and it is likely that this trend will continue in the future. The present volume follows this trend and offers a collection of systematically selected and original research papers on new economic perspectives on tourism research and policy. Most papers were initially presented at an international conference on tourism-economic research in Evora, Portugal in 2005. After a careful screening and revision process, this has led to the current volume with contributions by authors from various parts of the world. The editors recognize the support for this volume by ITP and Deloitte. Their enthusiastic role is highly appreciated. May 2007

´ Alvaro Matias Paulo Neto Peter Nijkamp

Contents

Trends in Tourism Research: Prefatory Remarks ´ Alvaro Matias, Paulo Neto and Peter Nijkamp . . . . . . . . . . . . . . . . .

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Part I New Analysis Frameworks in Tourism Economics Advances in Tourism Research: Theoretical Paradigms and Accountability Gayle R. Jennings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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The Influence of Immigration and International Tourism on the Import Demand for Consumer Goods – A Theoretical Model Christian Fischer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 An Economic Analysis of Tourism Contracts: Allotment and Free Sale Massimiliano Castellani and Maurizio Mussoni . . . . . . . . . . . . . . . . 51 Is Tourism Specialization Sustainable for a Small Island Economy? A Cyclical Perspective Sauveur Giannoni and Marie-Antoinette Maupertuis . . . . . . . . . . . 87 Efficiency in a Chain of Small Hotels with a Stochastic Production Frontier Model ´ Carlos Pestana Barros and Alvaro Matias . . . . . . . . . . . . . . . . . . . . . 107

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Part II New Operational Tools in Tourism Research Destination Competitiveness: Meeting Sustainability Objectives Through Strategic Planning and Visioning Lisa Ruhanen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 International Tourism and Economic Growth: A Panel Data Approach Tiago Neves Sequeira and Carla Campos . . . . . . . . . . . . . . . . . . . . . . 153 Benchmarking in Tourism Destinations; Keeping in Mind the Sustainable Paradigm Valentina Bosetti, Mariaester Cassinelli and Alessandro Lanza . . 165 Microeconomic Determinants of the Duration of Stay of Tourists Joaqu´ın Alegre and Lloren¸c Pou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 Multicriteria Evaluation and Local Environmental Planning for Sustainable Tourism Andrea De Montis, Giancarlo Deplano and Peter Nijkamp . . . . . . 207 Strategic Planning of Territorial Image and Attractability Paulo Neto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 Part III Applied Country and Regional Studies A Comparison of Methods for Assessing the Short-Run Economic Impacts of Tourist Spending on a County Economy Brian VanBlarcom and Kenneth F. Backman . . . . . . . . . . . . . . . . . . 259 Measuring the Impact of Tourism on Production by Means of an Input-Output Model of Interior Flows. An Application to Galicia Lu´ıs Casta˜ n´ on and Xes´ us Pereira . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275 On “E-Attraction” Tourism Destination – Extension and Application Nicolas Peypoch and Bernardin Solonandrasana . . . . . . . . . . . . . . . . 293

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The Use of the Internet in the Hotel Sector of the Balearic Islands: Evolution and Perceptions ` Vich-i-Martorell and Lloren¸c Pou . . . . . . . . . . . . . . . . . . 307 Gabriel A. Efficiency and Productivity of Italian Tourist Destinations: A Quantitative Estimation Based on Data Envelopment Analysis and the Malmquist Method Maria Francesca Cracolici, Peter Nijkamp and Miranda Cuffaro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325 A Night at the Opera Festival: The Economics of Opera Stephen Wanhill . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 List of Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367

Trends in Tourism Research: Prefatory Remarks ´ Alvaro Matias,1 Paulo Neto2 and Peter Nijkamp3 1

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´ University of Evora, Urban and Regional Analysis and Research Laboratory ´ Largo dos Colegiais 2, Apartado 94, 7002-554 Evora, Portugal [email protected] ´ University of Evora, Department of Economics and Actae/RegUrb – Urban and Regional Analysis and Research Laboratory, Largo dos Colegiais, 2, 7000-803 ´ Evora, Portugal [email protected] Free University, Department of Economics, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands [email protected]

Tourism will most likely be one of the fastest growing economic sectors in our century. This holds for both domestic and international tourism. For example, since World War II the volume of international tourism has increased with a factor 30 (see UN WTO 2006). And domestic tourism has even risen more. Tourism is also a source of economic growth resulting from expenditures on travel, accommodation, entertainment etc. A variety of background factors may explain this unprecedented growth in tourism: improvement in transport systems and infrastructure, new information technology and logistics, increase in wealth and disposable income in large parts of our world, new lifestyles and more leisure time, and international openness and globalization (see Prosser 1994, Urry 2002). Clearly, the rapid growth in long-haul tourism has also its shadow sides in the form of environmental externalities and threats to sustainable development (see e.g. Cooper and Lockwood 1994, Lindberg et al. 2001, and Sharpley 2000). Tourism is often regarded as a luxury good with a high price elasticity, so that it is sensitive to price differences and economic fluctuations. Consequently, we observe an increasing competition among tourist destinations. Supply and demand conditions form a complex force field and call for solid applied research. Amelung (2006) presents

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a consistent analytical and explanatory framework for tourism and distinguishes six background factors: demography (e.g. population growth and migrant flows), culture (e.g. leisure, fashion, hedonism), economy (e.g. discretionary income), technology (e.g. ICT, high-speed transport systems), environment (e.g. nature and climate), and institutions and politics (e.g. liberalization, global tourist operators). The economic impacts of tourism – both inbound and outbound – are formidable and ought to be traced systematically. They range from macro-economic effects (such as the balance of payment, taxes or employment) to micro-economic effects (such as the economic position of local shopkeepers or diversification of the local economy). In addition, the economic impacts have to be confronted with environmental impacts (such as emission of pollutants, sewage, biodiversity or landscape destruction) and socio-cultural impacts (such as local identity etc). In addition, the temporal and spatial impacts of tourism as a largely seasonal activity concentrated in a limited number of places has to be recognized. Consequently, there is a need for a solid and multi-faceted analysis of the drivers and impacts of tourism at various scales (from micro to macro). And fortunately, we have witnessed a significant progress in tourism research in the past decades, ranging from modeling economic growth arising from tourism to the design of tourist satellite accounts. The research field of the economics of tourism is still in full motion and rapidly developing. The research agenda of tourism is vast (see e.g. Crouch 1994, Eilat and Einev 2004, Eugenio-Martin 2003, Giaoutzi and Nijkamp 2006, or Smeral and Weber 2000). It covers many items, ranging from macro(or meso-)economic research on the importance of the tourist sector or ecological sustainability threats to local or global quality of life to micro-behavioral research on motives or spending patterns of tourists (see also Alegre and Pou 2004, Eymann and Ronning 1997, Giaoutzi and Nijkamp 2006, Smith and Krannick 1998, Swarbrooke 2002). There is thus a need for statistical information on the volumes, the transport patterns and modal choices, or the destination choices and expenditures of tourists, as well as on the supply of accommodations, the tourist infrastructure and the nature of tourist products (ranging from nature or beaches to cultural heritage or festivals). There is also a need for strategic insights into structural changes in the tourist sector, such as the rise of low cost carriers, changes in the tour operators’ branch, the

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impacts of changing life styles (e.g. multiple short holidays), or the threats for sustainable tourism development. In light of the previous observations, the present volume on ‘Advances In Tourism Research – Economic Perspectives’ aims to highlight and to map out new research challenges and trends in the rapidly growing field of tourism economics and management. This volume finds its origin in an international conference on new pathways in the economics of tourism, held at the University of Evora in May 2005. After a careful screening and judgment of the wealth of contributions, the editors decided to include a series of selected promising papers in book format, after a careful review process. This selection led to the present edited volume which comprises mainly revised papers which were received after the conference. This volume is organized in the following manner. The first part of this volume addresses the need for appropriate new analytical frameworks for economic research on tourism. We will briefly summarize the various contributions in this part of the book. Jennings focuses attention on theoretical paradigms and accountability in modern tourism research and management, in particular paradigmatic and methodological research on e.g. mixed qualitative-quantitative issues, interdisciplinary and stakeholders approaches, cross-cultural capacity building and local-regional development. Next, Fischer presents a theoretical framework on the role of immigration and international tourism on the import demand for consumer goods, in which changes in consumers’ preferences are linked to demographic shifts and international mobility of tourists. The complexity of the tourism market is next investigated by Castellani and Mussoni, who analyze tourism contracts regulating the business linkages between tourist operators and hotels (in particular, allotment and free sale contracts). In a subsequent chapter, Giannoni and Maupertuis offer a cyclical perspective on sustainable tourism development in small isolated economics, by designing a dynamic tourist model in which the intertemporal trade-off between tourism-intensive investments and environmental quality preservation in the long run is studied. A final contribution in this part is provided by Barros and Matias who analyze the technical efficiency of the hotel sector on the basis of a Cobb-Douglas production frontier model. The above collection of new analytical contributions demonstrates the wealth of new research endeavours on the economics of tourism.

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The second part of the present publication is devoted to a presentation of new operational tools in modern tourism research. First, Ruhanen studies the competitiveness among tourist destinations by paying attention to sustainability conditions on the basis of strategic visioning. In the next article, Neves Sequeira and Campos address economic growth in relation to international tourism by deploying a panel data approach, in which also R&D intensity and scale economies are incorporated. The differences in tourism management performances are next analyzed by Bosetti, Cassinelli and Lanza on the basis of a benchmarking approach connected with a data envelopment analysis model. Tourist behaviour is an important element in tourism research and this issue is studied by Alegre and Pou, with the help of a discrete microeconomic model in which the duration of stay is explained from the tourist’s socio-demographic profile and the holiday characteristics. De Montis, Deplano and Nijkamp offer then an operational multicriteria evaluation model to support local environment planning for sustainable tourism. And finally, Neto presents an original analysis of territorial images and attractability factors in the tourism sector. This part demonstrates convincingly that the development of new research tools in tourism research is booming and creates the conditions for advanced applied work. The third and final part of the volume is dealing with applied country and regional studies with a strong analytical basis. The first contribution here is offered by VanBlarcom and Backman who present the results of an input-output model to assess the economic impacts of tourism expenditures in Kings County, Nova Scotia, Canada. Another study on input-output analysis is provided by Castanon and Pereira who study the economic effects of incoming tourism in Galicia. Next, Peypoch and Solonandrasana investigate the implications of ICT on the attractiveness profiles (the ‘e-attraction’) of the Languedoc-Roussillon region in France. Internet as a strategic tool in the tourism sector (in particular, the hospitality sector) has gained much popularity, and Vich-i-Martorell and Pou analyze the use of internet in the hotel sector on the Balearic Islands. A subsequent empirical study is offered by Cracolici, Nijkamp and Cuffaro, who made an extensive analysis of the efficiency and productivity of Italian tourist destinations using various frontier methods. Finally, Wanhill provides an interesting economic analysis of the opera sector, with a particular view on the Savonlinna

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Opera Festival. At the end of this part, it is also evident that the mix of advanced methods and case studies leads to promising perspectives for future research in the economics of the tourism sector.

References UN WTO, World Tourism Barometer, World Tourism Organization, Madrid, vol. 4, no. 1, 2006. Prosser, R., Societal Change and the Growth in Alternative Tourism, Ecotourism: A Sustainable Option? (E.Cater & G.Lowman, eds.), John Wiley, Chichester, 1994, pp. 91–114. Sharpley, R., Tourism and Sustainable Development, Journal of Sustainable Tourism, vol. 8, no. 1, 2000, pp. 1–19. Urry, J., The Tourist Gaze, Sage, London, 2002. Amelung, B., Global (Environmental) Change and Tourism, Ph.D. Thesis, University of Maastricht, 2006. Swarbrooke, J., Sustainable Tourism Management, CABI Publishing, New York, 2002. Cooper, C., and A. Lockwood (eds.), Progress in Tourism, Recreation and Hospitality Management, Belhaven, London, 1994. Lindberg, K., T.D. Andersson and B. Dellaert, Tourism Development, Annals of Tourism Research, vol. 28, no. 4, 2001, pp. 1010–1030. Smith, M.D., and R.S. Krannick, Tourism Dependence and Residents Attitudes, Annals of Tourism Research, vol. 25, no. 4, 1998, pp. 783–802. Giaoutzi, M., and P. Nijkamp (eds.), Tourism and Regional Development: New Pathways, Ashgate, Aldershot, UK, 2006. Alegre, J.A., and L. Pou, Micro-economic Determinants of the Probability of Tourism Consumption, Tourism Economics, vol. 10, no. 2, 2004, pp. 125–144. Crouch, G.I., The Study of Tourism Demand: A Review of Findings, Journal of Travel Research, vol. 33, no. 1, 1994, pp. 12–23. Eilat, Y., and L. Einev, Determinants of International Tourism: a Threedimensional Panel Data Analysis, Applied Economics, vol. 36, 2004, pp. 1315–1327. Eugenio-Martin, J.L., Modelling the Determinants of Tourism Demand as a Five-stage Process: A Discrete Choice Methodological Approach, Tourism and Hospitality Research, vol. 4, no. 4, 2003, pp. 341–354. Eymann, A., and G. Ronning, Microeconometric Models of Tourists’ Destination Choice, Regional Science and Urban Economics, vol. 26, no. 6, 1997, pp. 735–761. Smeral, E., and A. Weber, Forecasting International Tourism Trends to 2010, Annals of Tourism Research, vol. 27, no. 4, 2002, pp. 982–1006.

Part I

New Analysis Frameworks in Tourism Economics

Advances in Tourism Research: Theoretical Paradigms and Accountability Gayle R. Jennings Griffith University, Griffith Business School, Department of Tourism, Leisure, Hotel and Sport Management, Gold Coast Campus, Queensland, Australia [email protected]

1 Introduction The purpose of this paper is to reflect on advances in tourism research and related accountability issues for tourism economics and management. In particular, the paper focuses on a range of theoretical paradigms that may inform research in the areas of tourism economics and management in a twenty first century environment. The thesis of this paper, is that research in tourism economics and management has been predicated to western-based epistemologies as well as a positivistic and postpositivistic hegemony and that such epistemologies and hegemony no longer represent an accountable tourism research agenda in a twenty-first century world of flux and unpredictability. In pursuing this thesis, the paper is divided into seven sections: an overview of tourism economics and management regarding paradigmatic and methodological research; reflections on the status of paradigmatic and epistemological research; consideration of theoretical paradigms that may inform research in tourism economics and management; a discussion of the action research and heuristic methods used to prepare this paper; an example of an accountable research agenda which promotes advances in research in tourism economics and management; the proposal of a generic research strategy to advance tourism economics and management in an accountable manner; as well as concluding remarks.

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2 Overview of Tourism Economics and Management Regarding Paradigmatic and Methodological Research Traditionally, tourism economics and management research has applied western based epistemologies to examine the various patterns, components, sectors, activities, experiences, and peoples involved in the phenomenon of tourism from a positivistic or postpositivistic theoretical paradigm, particularly, the tradition of critical realism. Riley and Love (2000) report that quantitative based research (rooted in positivistic and postpositivistic paradigms) is the dominant form of research published in travel and tourism journals; a view also purported earlier by Cohen (1988) and Walle (1997). Such publications then serve as models and demonstrations of ‘acceptable’ (accountable) research and the paradigms are reified as orthodoxy for the travel and tourism industry, tourism academia, as well as related industries, associations and bodies. Subsequently, this orthodoxy makes them the hegemonic (dominant) paradigms against which all other travel and tourism research is benchmarked. To exemplify this statement, most travel and tourism journals and conference calls reinforce the positivistic and postpositivistic hegemony by nature of the headings researchers must address in reporting of research: introduction, background, literature review, methods, findings, results and recommendations. These headings are predicated on the ‘hard’ sciences report writing genre emanating from positivistic and postpositivistic theoretical paradigms. A genre also associated with the related business journals in which tourism researchers may publish (see Sheldon (1990), and reflective of the disciplines in which a number of tourism researchers were initially grounded (see Dann, Nash, Pearce, 1988, e.g. of these). Other theoretical paradigms utilise different genres such as the narrative for reporting research. However, researchers utilising paradigms drawn from outside the hegemonic ones are generally required to comply with the orthodox genre. An investigation of basic research textbooks, which are used in western-based tourism and related business disciplines research training and education systems, further demonstrates this reification, specifically, in regard to positivism and postpositivism and the subsequent emphasis on a quantitative methodology. Compare for example the quantitative emphasis in the following tourism textbooks: Ritchie and Goeldner (1994), Smith (1995), Veal (1997), Finn (2000) to the publications by Jennings (2001), Goodson and Phillimore (2004) and

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to a lesser extent Ritchie, Burns and Palmer (2005). Again, compare the quantitative emphasis located in related business research texts: Marcoulides (1998), Davis (2000), Ticehurst and Veal (2000), Cavanna, Delahaye, and Sekaran (2001), Malhorta, Hall, Shaw and Oppenheim (2002), Cooper and Schindler (2003), Hair, Babin, Money and Samouel (2003), Sekaran (2003) and Zikmund (2003), and the attempts at balance in Robson (2002), Remenyi, Williams, Money and Swartz (1998), Collis and Hussey (2003) with the qualitative leanings in the works of Belkaoui (1987) and especially, Gummesson (1999). Essentially, the research curriculum in tourism (see Tribe, 2001) and business related disciplines eschew qualitative and indigenous methodologies. Furthermore, representation of the use of a qualitative methodology in core tourism and business research textbooks tends to be somewhat pejorative and/or dismissive in regard to the nature of its potential contribution to research enterprises. For example, Malhotra (2001) contains a chapter entitled “Exploratory research design: qualitative research”. The accompanying promotional blurb reads “A whole chapter is dedicated to qualitative research.”; that is, one chapter of nineteen. Another viewpoint promulgated is Ernest Rutherford’s alleged claim that “Qualitative is poor quantitative” (Stewart, 1997, p. 205). Consequently, travel and tourism journals, conference calls and basic tourism research and related business textbooks replicate and perpetuate the orthodoxy of the hegemonic paradigms and work to constrain use of creative and innovative research methods to understand travel and tourism phenomena.

3 Reflections on the Status of Paradigmatic and Epistemological Research in Tourism Economics and Management As already noted, travel and tourism research is grounded in an historical research background of positivistic or post-positivistic research paradigms. Subsequently, it is also predicated to a quantitative research methodology (Riley and Love 2000) due to the contributing disciplines and their paradigmatic biases. Furthermore, this historical context and associated influences have resulted in criticism of tourism research for mono-disciplinary and/or fragmented research enterprises using multidisciplinary rather than interdisciplinary approaches (refer to Jafari 1977; Leiper 1981; Stear 1981; Przeclawski 1993; Echtner

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& Jamal, 1997; Tribe 1997 for debates regarding this issue). This is not to say that other paradigms, methodologies, or interdisciplinary approaches are not utilised [see e.g. Hollinshead (1996); Jamal and Hollinshead (2001)], rather they are not part of the dominant hegemonic practices in travel and tourism research. Additionally, travel and tourism research has been viewed through primarily western-based epistemologies which have tended to marginalise or silence alternate epistemological viewpoints. How accountable is such an approach which continues to privilege one worldview over others within an ever-changing and unpredictable world environment? Moreover, is the representation of tourism phenomena using dualist – either or principles, linear-causal relationships between phenomena or statistically determined formulae enough? No, it is the premise of this paper that tourism is a complex and multiple phenomena and so needs to be considered in a more holistic way rather than in segmented and controlled contexts and experiments. Tourism is a socially constructed and determined phenomena that is constantly being reframed and reinterpreted and reconstructed. It is in a constant state of processing and flux with ongoing meaning making/sense making and reframing within and between a variety of cultural contexts. Subsequently, along with the predominance of positivistically informed research; the dominance of western-centric epistemologies needs also to be challenged. Why, because travel and tourism in the twenty first century is markedly different from travel and tourism in the second half of the twentieth century. While it is a world context that continues to be influenced by the dominant themes from the 1980s, particularly, the impacts of globalisation, internationalisation, and participation in the knowledge economy; it is also influenced by the impacts of an increasing number of unexpected events resulting from natural and human influences. Still further, and as already noted, the current world context in which the travel and tourism industry now operates is one of rapid change, instability and unpredictability. In a world of such uncertainty, reliance on past (hegemonic/dominant) practices and historical patterns is proving problematic for explaining current and future tourism patterns and phenomena. New and different methodologies and methods need to be used to provide (flexible, insightful and at times rapid) responses to travel and tourism issues to better serve the industry, theory development, the understanding the phenomena of travel and

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tourism across multiple cultural sites and settings as well as epistemological viewpoints. Thus, this paper argues that it is not accountable for the current and future world of global interconnectivity and situatedness to privilege positivism and postpositivism and western-centric paradigms and epistemologies. In taking this stance, the paper examines this argument in more detail. To reiterate, the purpose of this paper is to consider advances in research in the fields of tourism economics and management and related accountability issues, particularly the relevance of perpetuating a hegemonic research discourse predicated on westerncentric viewing lenses and positivistic and postpositivistic paradigms, which marginalise other paradigms. Having provided background context, the paper will now overview a suite of paradigms that might be used to inform tourism and travel research.

4 Consideration of Theoretical Paradigms that May Inform Research in Tourism Economics and Management This section focuses on seven theoretical paradigms from which a researcher may select when undertaking tourism economics and management research (Jennings, 2001): a positivist/postpositivistic approach, a chaos theory/complexity theory orientation, an interpretive social sciences approach, participatory paradigm, critical theory approach, feminist perspectives, or a postmodern approach. The tenets of each paradigm (see Guba 1990), specifically, the ontology (world view), epistemology (relationship between the knower and the known), methodology (quantitative or qualitative) (Guba and Lincoln, 1994) and axiology (values and ethics) (Lincoln and Guba, 2000), are briefly described in the following subsections and overviewed in Table 1: Overview of paradigms that may inform tourism economics and management research. Positivism, as a means to study the social world, is a paradigm grounded in the physical sciences. It was founded by the French philosopher, Auguste Comte (1798–1857), who also coined the term positivism. What is the intent of positivism: “In the final, positive state, the mind . . . applies itself to the study of . . . laws, -that is their invariable relations of succession

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and resemblance. Reasoning and observation, duly combined, are the means of this knowledge. What is now understood when we speak of an explanation of facts is simply the establishment of a connection between single phenomena and some general facts, . . .” (Comte, 2000, p. 28) More particularly and ontologically, the natural and social world is governed by ‘universal’ laws. The real world is a closed system, which is stable and patterned, that is, reality may be determined and subsequently, behaviour and events may be predicted. Epistemologically, positivism is objective and utilises quantitative methodologies. Axiologically, positivism is intrinsic, propositional and value neutral. Over time, the deterministic stance of positivism has been critiqued and as a response to that critique the paradigm of postpositivism developed. Like positivism, postpositivism also considers that there is a ‘reality’ though postpositivists acknowledge that this is imperfectly and probabilistically determined (Robson, 2002). The epistemological position is objective, however, postpositivists also acknowledge that the knowledge and experiences of the researcher may influence results despite attempts at objectivity and such biases are acknowledged. Methodologically, postpositivism is primarily predicated on quantitative methods; however, mixed methods are utilized with a continuing emphasis on internal and external validity as well as reliability. As was the case with positivism, the axiology of postpositivism is propositional, intrinsic and objective although, as already noted, the influence of researchers and any subsequent bias in research design is acknowledged. A related tradition of postpositivism is critical realism, which is based on the work of Bhasker (1978, 1982, 1990) and Harr´e (1981, 1986). Bhasker (1986) purports that critical realism should have an emancipatory role (axiology). Such a role differs from positivism. Whilst I have positioned critical realism under the banner of postpositivism, I note that Byrne (1998) does not consider it as either positivist (or by association reductionist) or phenomenological. I will, however, locate critical realism within postpositivism Jennings (2004). The Chaos Theory/Complexity Theory Paradigm emerged from the works of Edward Lorenz, Michel H´enon, Robert May, Benoit Mandelbrot, Mitchell Feigenbaum (Gleick, 1987). Chaos and complexity are related to realism and so have connections with postpositivism (Byrne, 1998). Chaos theory views the world as non-linear and dynamic in nature (ontology). A world, in which, small changes

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can generate large-scale outcomes. Scientific inquiry is based on descriptive algorithms as well as non-linear and non-integral systems although some social sciences utilize chaos theory in a metaphoric sense. The epistemological position of chaos theory essays for objectivity while methodologically, the paradigm moves between quantitative (algorithms) to qualitative (use of chaos as a metaphor) methodologies. McKercher (1999) provides a commentary on the use of chaos theory as a tool to understand the tourism system. Related to chaos theory is complexity theory. Complexity theory has an ontological position that portrays the world as constituted of complex systems that move rapidly from chaos to order through self-organising processes (Rubinstein and Firstenberg, 1999). Axiologically, chaos and complexity are intrinsically and interactionally situated. Both attempt to engage with: “. . . a past of conflicting certitudes, be they related to science, ethics or social systems, to a present of considerable questioning, including questioning about the intrinsic possibility of certainties. . . . the end of a type of rationality that is no longer appropriate to our time. . . . [to an accent] on the complex, the temporal and the unstable, which corresponds to a transdisciplinary movement . . .” (Gulbenkian Commission 1996, p. 79). The Interpretive Social Sciences Paradigm (ISS) is associated with social constructionism, social phenomenology and social constructivism (Jennings 2004). The roots of constructionism are associated with William Thomas (1863–1947), social phenomenology with Alfred Schutz (1899–1959), who drew upon the philosophical phenomenology of Edmund Husserl (1859–1938). Peter Berger (1925–) and Thomas Luckmann (1927–), both students of Schutz, developed social constructivism while the interpretive social science paradigm is associated with the work of Max Weber (1864–1920) and Wilhem Dilthey (1833– 1911). The interpretive social sciences paradigm and related approaches reflect that: . . . [h]uman beings do not find or discover knowledge so much as we construct or make it. We invent concepts, models, and schemes to make sense of experience, and we continually test and modify these constructions in the light of new experience. Furthermore, there is an inevitable historical and sociocultural dimension to this construction. We do not construct our interpretations in isolation but against a backdrop of shared understandings, practices, language, and so forth. (Schwandt, 2000, p. 197)

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Relatedly, ISS has an ontological position which acknowledges the multiple realities of the people (sometimes called actors) participating in the research. Consequently, the ontological position of interpretive social scientists with its multiple realities is oppositional to postpositivists who perceive a ‘reality’. The epistemological position of ISS is a subjective and value-laden one. Moreover, researchers utilize primarily a qualitative methodology and engage in an intrinsic, instrumental and transactional axiology. A variety of traditions are associated with the interpretive social sciences approach, such as, phenomenology, ethnomethodology, ethnology, symbolic interactionism, and ethnography. The Participatory Paradigm is grounded in “liberation theology and neo-Marxist approaches to community development” as well as “human rights activism” (Kemmis and McTaggart, 2000, p. 568). The term action research was coined by Lewin (1948). “Three particular attributes are often used to distinguish [participatory research, PR] PR from conventional research: shared ownership of research projects, community-based analysis of social problems, and an orientation toward community action.” (Kemmis and McTaggart, 2000, p. 568). The participatory paradigm maintains an ontological perspective that reality is individually and collectively known (Reason 1994), an epistemology in which experiential learning arises from participation and self-reflexive directed actions, and a methodology that is essentially qualitative although mixed methods may be used (Jennings 2001). Its axiological position is intrinsic, interactional, emancipatory and cooperative in nature. A number of types of action research exist, such as, cooperative inquiry, participatory action research, action inquiry, appreciative inquiry (Jennings, 2004). The Critical Theory Paradigm is based upon the works of Karl Marx (1818–1883). It adopts an ontological position that the social world is constrained by rules, although these rules can be changed. In particular: “Critical forms of research call current ideology into question, and initiate action, in the cause of social justice. In the type of inquiry spawned by the critical spirit, researchers find themselves interrogating commonly held values and assumptions, challenging conventional social structures, and engaging in social action. Fuelling this enterprise is an abiding concern with issues of power and oppression. Critical inquiry keeps the

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spotlight on power relationships within society so as to expose the forces of hegemony and injustice. It is at all times alive to the contribution that false consciousness makes to oppression and manipulation and invites researchers and participants (ideally one and the same) to discard false consciousness, open themselves to new ways of understanding, and take effective action for change.” (Crotty, 1998) Its epistemological perspective is half-way between subjectivism and objectivism. Axiologically, critical theory should lead to transformational change as the aim of research in this paradigm is to alter the social circumstances of those being studied. In general, a qualitative research methodology is applied. The Feminist Perspectives Paradigm from a western perspective is vested in three waves of feminism (Hill n.d.). The first wave (circa the nineteenth century) was associated with resolving social and legal inequalities experienced by women. The second wave (circa the late 1960s) signifies women’s responses to anti-discrimination via activities strategies, such as protests, demonstrations, strikes, and the formation of women’s groups. The third wave occurred in the 1990s and was a response by young women (15–30) to use using different mechanisms to address discrimination and to be women in different ways to their Second Wave counterparts. However: “[t]his is a good place to note that the term “feminism” is itself a contested zone not only within feminism but also between feminism and its critics.” (p. 6) . . . “If androcentric and Eurocentric beliefs and practices are part of the evidence for one hypothesis over another (inadvertently or not), then as part of scientific practice we must learn how to detect and eliminate them.” (Harding, 1991, p. 15) The feminist perspectives paradigm aims to highlight the lived experiences of women and to challenge or break down the dominant patriarchal hegemonic view of the world (ontology). This paradigm has linkages with the interpretive, critical theory and postmodern paradigms. There is some debate regarding feminist perspectives being a paradigm in their own right or solely a perspective of the previous two paradigms. In the main, qualitative methodologies are adopted and the researcher and the researched are subjects together resulting in an epistemology which is subjective, and an axiology that is propositional, transactional, instrumental and of intrinsic value. There

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Table 1. Overview of paradigms that may inform tourism economics and management research Positivism

PostPositivism

Critical Realism

Critical Theory

Social Feminist Constructivism Perspectives

Postmodern

Participatory

Synonyms and/or related terms

Empiricism Realism Na¨ıve realism Objectivism, Foundationalism Representationalism

New realism (Note: Developed as a response to critique of positivism.)

Described as a midpoint between realism and relativism.

A number of types: Marxist/Socialist Postpositivist Postmodern critical theorists.

Phenomenology Interpretivism Constructivism Constructionism

Ludic postmodernism Oppositional postmodernism Critical postmodernism

Cooperative inquiry Participatory action research Action inquiry Appreciative Inquiry

Ontology

Truths and laws are universal

Truths are fallible and a product of historical and social contexts.

Truths are fallible and a product of historical and social contexts.

Sociohistorical multiple realities. Realities reflective of power relations.

Multiple perspectives/realities

Multiple realities. No privileging of position. Skepticism towards ‘truth’ and ‘-isms’

Multiple realities collectively constructed via interactions.

A number of types: Marxist/Socialist Liberal Postmodern Poststructural Critical Feminist empiricism Standpoint theorists Multiple realities mediated by gendered constructs

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Paradigm Descriptors

Epistemology

Methodology

Axiology

Objective

Objective acknowledges potential for researcher bias Quantitative Inclusion of mixed methods Essays to be value free Extrinsic purpose of research project. Consideration of emancipatory role of research.

Subjective unless postpositivist critical theory (objective) Qualitative Some quantitative Value laden. Intrinsic focus of research projects. Political agendas. Emancipatory. Transformative.

Subjective

Subjective Intersubjectivity Subjective Co-researchers Objective (Participants and researcher/s)

Qualitative

Qualitative (Predominantly)

Qualitative

Value laden. Intrinsic focus of the research project.

Value laden. Intrinsic focus of research projects. Political Emancipatory Transformative Educational

Sceptical of emancipation and transformation. Continuous deconstruction process.

Qualitative Quantitative Mixed Method Value laden. Transformation.

Source: Adapted from Jennings, G.R. (2004) Business Research, Examples of Theoretical paradigms that inform, Encyclopedia of Social Measurement, San Diego, CA: Academic Press, pp. 211–217.

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Objective Acknowledges potential for researcher bias Quantitative Quantitative (Use of mixed methods) Value free Essays to Extrinsic be value purpose of free research Extrinsic project. purpose of research project.

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are a variety of feminist approaches, such as, radical feminism, liberal feminism, socialist/Marxist feminism, poststructuralist feminism, postmodern feminism. The Postmodern Paradigm is associated with the works of JeanFran¸cois Lyotard (1924–1998) and Jean Baudrillard (1929–) as well as Jacques Lacan (1901–81), Roland Barthes (1915–80), Michel Foucault (1926–84), and Jacques Derrida (1930–2004). Frequently misunderstood, reviled, or faddishly embraced (Best and Kellner 1991), postmodernism offers an idiom for characterizing lived experience that challenges, if not subverts, traditional forms of empirical description. Paradoxically, the lack of unity within postmodernism reflects one of its most widely shared tenets: the possibility of certainty must be regarded skeptically, if not rejected outright. This reverberates throughout the social sciences as a challenge to comprehensive or veridical descriptions of experience. Postmodernism casts doubt on the possibility of any totalizing or exhaustive theories or explanations (Gubrium and Holstein, 2003, p. 4). Postmodern researchers dispute grand theory and views the world (its ontological perspective) as being constructed of multiple realities and that no one reality has favour over another. A central tenet is the deconstruction of the surface features of phenomena in order to expose the underlying core realities. A variety of methods are used, which are generically derived from a qualitative methodology. Axiologically, the ‘postmodern’ paradigm is propositional, transactional, instrumental, and intrinsic in its values and ethical stance. There are a variety of approaches such as, ludic postmodernism oppositional postmodernism and critical postmodernism.

5 Methods The premise of this paper is informed by action research from 1995 to the present while I have been actively engaged in tourism economics and management research and training. Throughout this period, I engaged in repetitive cycles of action research: planning, implementing, monitoring and reflecting (Kemmis and McTaggart 1988) on the theoretical paradigms used in tourism research projects. Heuristic research (Moustakas, 1990) was also used. Heuristic research (Moustakas 1990) is associated with empirical material (data) collection of experiences

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and literature, self-reflection and interpretation (analysis) then synthesis of key components of the phenomenon being studied (in this case, tourism economics and management research). Specifically, heuristics involves “initial engagement, immersion in the topic and question, incubation, illumination, explication and culmination of the research in a creative synthesis” (Moustakas 1990, p. 27). This may be rephrased as empirical material collection, self-reflection, interpretation and synthesis (note the synergy with action research). For the purposes of this paper, ‘initial engagement’ with the topic of theoretical paradigms that inform tourism economics and management research occurred when I was a student, an educator and a researcher. ‘Immersion’ resulted through my participation in tourism economics and management research using the various paradigms. ‘Incubation’ regarding a hegemonic paradigm of tourism economics and management research and the marginalisation of other theoretical paradigms and epistemologies happened during both teaching research methods and engaging in research. ‘Explication’ occurred during the last eight years of teaching research classes. ‘Creative synthesis’ took place in the process of writing this paper and other publications.

6 An Example of an Accountable Research Agenda to Promote Advances in Research in Tourism Economics and Management In this paper, seven theoretical paradigms have been highlighted. These represent a suite of paradigms from which a tourism economics and management researcher may select when undertaking tourism economics and management research: a positivist/postpositivist approach, chaos/complexity theory orientation, an interpretive social sciences approach, participatory paradigm, critical theory approach, feminist perspectives, and a postmodern approach. To exemplify a multiparadigmatic as well as interdisciplinary study, the Bali Bombing, 12 October 2002 incident is used as the focus for reflecting upon such an agenda. Each of the aforementioned paradigms will be overviewed in turn along with suggested methods, a rationale for inclusion and anticipated outcomes that might be achieved. It must be emphasised that the following overviews represent possible proposals founded on lived experiences (Van Manen, 1990) and heuristics. As such they are a (re)presentation rather than a definitive and complete agenda since the projects have not

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been dialogued with interested stakeholders to co-construct them or to identify other areas of interest and need. Nor do they incorporate any indigenous methodologies or epistemologies at this point, although I do indicate where this might be included in order to represent a starting point for dialogue. Positivism/Postpositivism (Critical Realism) Project Title: Telephone survey of attitudes of Australian residents regarding travel to Bali. Methods: Interviewer completed questionnaire using computer assisted telephone interviewing (CATI) technology. Team building capacities and cross-cultural communication capacity building, monitoring and responding to power dynamics with regard to the research team. Multigenre outputs. Rationale for Use: Establish baseline data, monitor over time as a longitudinal study. Outcome: Knowledge of information sources and decision making by Australians, a key originating market for Bali. Outcomes useful to tourism authorities and interested stakeholders. Chaos Theory and Complexity Theory (Complexity Theory) Project Title: Study of the self-organising practices of Balinese groups post the bombing. Methods: Participant observation and interviews. Researcher in-country learning and training – language and cultural understanding and mores, the development and use of indigenous epistemologies and methodologies, team building capacities, monitoring and responding to power dynamics. Multi-genre outputs. Rationale for Use: Use of a paradigm that matches the study focus and resultant synergy in research design and findings. Outcome: Mapping of the actions and processes of Balinese groups and the resulting effects or changes to everyday life and tourism. Interpretive Social Sciences Project Title: Bali post-bombing: the lived experiences of travellers.

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Methods: Ethnography, participant observation, semi-structured indepth interviews, questionnaires. Empirical material interpretation using “grounded theory analysis” of qualitative materials and descriptive statistics for quantitative data. Researcher in-country learning and training – language and cultural understanding and mores, the development of indigenous epistemologies and methodologies, team building capacities and monitoring and responding to power dynamics. Multigenre outputs. Rationale for Use: Understanding of lived experience and development of emic (insider) understandings. Outcome: Understanding of travel experiences. Use of information for promotion and marketing, service delivery, market knowledge, as well as information provision to intending travellers regarding safety and risk management practices. Participatory Paradigm Project Title: Development of training and skilling in e-marketing, internet based-cum-desktop research. Methods: Action research involving the cycle of planning, implementing, monitoring and reflecting in conjunction with researcher in-country learning and training – language and cultural understanding and mores, the development of indigenous epistemologies and methodologies, team building capacities and monitoring and responding to power dynamics. Multi-genre outputs. Rationale for Use: Improvement of knowledge and practice in relation to tourism e-research skills by interested stakeholders. Outcome: Increased knowledge and awareness of competitors, and development of research skills. Critical Theory Paradigm Project Title: Bali post the bombing: perspectives of tourism industry workers. Methods: Ethnography, participant observation, semi-structured indepth interviews. Researcher in-country learning and training – language and cultural understanding and mores in conjunction with the development of indigenous epistemologies and methodologies, team building capacities and monitoring and responding to power dynamics. Multi-genre outputs.

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Rationale for Use: Gaining an in-depth understanding of tourism industry workers’ perspectives in order to highlight their lived experiences from multiple perspectives and effect change which would improve their lives. Outcome: Understanding of social, cultural, economic and technological changes for use in policy and planning. Feminist Perspectives Project Title: Impacts of the bombing on Balinese women and their entrepreneurial activities. Methods: Participant observation and semi-structured interviews, questionnaire for some demographic information. Interpretation involves successive approximation and analysis via basic descriptive statistics. Use of cross-cultural communication, trusted other, translator and interpreter of lived experiences. Researcher in-country learning and training – language and cultural understanding and mores. Development of indigenous epistemologies and methodologies, team building capacities, and monitoring and responding to power dynamics. Rationale for Use: To achieve an emic (insider’s perspective) and to foreground women’s experiences as being different to men’s experiences, especially, in regard to the processes associated with entrepreneurial activities and impacts on women’s work post the bombing. Outcome: In depth understanding of Balinese women’s experiences in tourism ventures and development of a typology of Balinese tourism entrepreneurial ventures. Postmodern Paradigm Project Title: An examination of post-Bali bombing discourses in the media and how the discourses served to influence tourist destination decision-making in Australia. Methods: Discourse analysis and semi-structured interviews. Team building, cross-cultural communication capacity building, as well as monitoring and responding to power dynamics. Multi-genre outputs. Rationale for Use: To understand how texts influence decision-making and how the media constructs and mediates ‘reality’ for Australians. The latter, whom constitute a primary market source for Balinese tourism.

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Outcome: Textual deconstruction and reconstruction to determine the impacts of media text selection and the power of the same to influence decision-making by potential Australian tourists. Each of these potential research projects utilises a different theoretical paradigm in order to exemplify an integrated research project to gain more holistic insight into the effects of the Bali bombing. In each instance, the most relevant rather than the hegemonic paradigm was utilised in order to achieve the best match between research purpose and research design.

7 A Generic Research Proposal to Advance Tourism Economics and Management in an Accountable Manner Having discussed the variety of paradigms which might be used to increase theoretical advances in tourism economics and management as well as an example of an integrated research agenda; the paper will now return to its key focus. Specifically, that research in tourism economics and management has been predicated to western-based epistemological lenses and a positivist/postpositivist hegemony. A hegemony, which this paper proposes is no longer an accountable research agenda in a twenty-first century world of flux and unpredictability. However, first, we need to revisit the current temporal world to further contextualise the latter statement. On the threshold of the twenty-first century, Potter and L´ opez (2001, p. 4) commented that: “It is the best of times. It is the worst of times. It is a time for the celebration of diversity. It is a time of fear of the Other who is different. It is a time of technological marvel and a time of fear and distrust of science. It is a time of unprecedented affluence and a time of the direst poverty. It is a time of nostalgia for the old and enthusiasm for the new. It is a time of optimism and hope for humanity’s possibilities of freedom and happiness and yet grim pessimism and fear about our future. It is a time of great intellectual achievement and also of the keenest awareness of the severe limitations inherent in the conditions of intellectual and scientific production.” It is a time not dissimilar to the closing decades of the twentieth century, a time, to which Naisbitt (1982) earlier referred as this ‘time of parenthesis’ – this time of great uncertainty and unpredictability. It has been and is a time in which travel and tourism researchers need to have

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diverse knowledge and skills in different research paradigms, in quantitative and qualitative methodologies and their supporting methods as well as in mixed methods, and in knowledge and skills of differing epistemologies and cultural standpoints. Why such an agenda to advance research in tourism economics and management? Because as Urry (1996, p. 369) commented much of social research in the twentieth century was devoid of consideration of space and time and that “social structures were viewed as consistent across space” and societies were not perceived as being constructed of “different social times”. Like Urry, I would argue that scant attention has been and is significantly paid to ethnicity, cultural identities, and gender and that our researcher gazes have been firmly entrenched in phallocentric, westerncentric, positivistic frameworks. I realise that such a view may not be shared by others, however: “A dominant group, inevitably, has the greatest influence in determining a culture’s overall outlook – its philosophy, morality, social theory and even its science. The dominant group, thus legitimises the unequal relationships and incorporates it into society’s guiding concepts . . . Inevitably that dominant group is the model for ‘normal human relationships’. It then becomes ‘normal’ to treat others destructively and to derogate them, . . . In short, if one’s identification is with the dominant group, it is ‘normal’ to continue in this pattern.” (Miller, 1976, pp. 6–8). Western epistemologies have constructed research agendas which have ignored tourism phenomena outside of our understanding of social and cultural constructions (Berno 1996; Urry, 1996). More broadly, Scheurich (1997), Stanfield II (1994), Bourke (1995), Ivanitz (1999), and Foley (2003) have criticised western epistemologies and research paradigms for similar biases. Additionally, Bourdieu (1990) and Laclau (1990) critique western-based research as imperialist or colonialist. As a consequence, and as Smith (1999, p. 15) argues there is a need for indigenous methodologies, since: “Indigenous methodologies tend to approach cultural protocols, values and behaviours as an integral part of methodology. They are factors to be built in to research explicitly, to be thought about reflexively, to be declared openly as part of the research design, to be discussed as part of the final results of a study and

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to be disseminated back to the people in culturally appropriate ways and in a language that can be understood. Relatedly, tourism researchers need to provide multi-genre outputs from research to cater for a diversity of stakeholder audiences.” Further, tourist behaviours and patterns need to be contextualised from both individual and group behaviours. Micro-macro applications are required (Jamal and Lee, 2003) emphasise the need to understand both the micro world of the tourist and the macro-home social setting in order to understand the tourist, especially to understand the role of the social construction of knowledge, because as Harding has commented “all scientific knowledge is always, in every respect, socially situated” (Harding, 1991, p. 10). Moreover, in order to advance tourism research in tourism economics and management, travel and tourism researchers need to understand the paradigms of positivism or postpositivism, a chaos theory/complexity theory orientation, interpretive social sciences, critical theory, feminist perspective, and post-modern approaches. Travel and tourism researchers need to also understand that the theoretical paradigm that informs the research process will subsequently influence the methodology that is selected due to each theoretical paradigm’s ontological, epistemological and axiological viewpoints. Researchers may select from either qualitative, quantitative or mixed method methodologies. Whilst Riley and Love (2000) note that most research in travel and tourism utilises a quantitative methodology, qualitative methodologically informed research can also contribute to understanding the phenomena of tourism. For travel and tourism researchers, decisions should not be based on which is the better methodology, but which methodology is the best to understand the tourism phenomena under study and this in turn links back to understanding of theoretical paradigms (Jennings, 2001). In choosing between a qualitative or quantitative approach (or a mixing of methods), travel and tourism researchers need to consider which of the following (or some combination if using mixed method) are more appropriate to the research study about to be undertaken: • the use of a deductive (quantitative) or an inductive (qualitative) approach; • causal relationships (quantitative) or multiple realities (qualitative);

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• objective relationship (quantitative) or subjective relationship (qualitative) between researcher and researched; • etic (outsider/quantitative) or emic (insider/qualitative) perspective; • random (quantitative) or non-random (qualitative) sampling; • numerical units of data (quantitative) or textual units (qualitative); • statistical representations (quantitative) or key themes/motifs identified (qualitative); • third person passive voice (quantitative) or narrative, first person active voice (qualitative) report writing; • findings inferred to population (quantitative) or representation of a slice of life (qualitative) (Jennings, 2001). A short note regarding mixed methods needs to be made. Some researchers would dispute mixing methods because of the incommensurability of the paradigmatic ontologies (see Jennings, 2004). That being said, the mixing of methodologies is usually associated with different phases of research designs, specifically, before, after and sometimes concurrent implementations (see Brannen, 1992; Tashakkori and Teddlie, 1998; Creswell, 2003 for further information). While it is acknowledged in travel and tourism research training courses that tourism economics and management researchers need to develop a repertoire of skills in and understanding of quantitative data collection methods, such as, “questionnaires and structured interviews, longitudinal studies, case studies, documentary method, modelling, observation, as well as impact assessment methods” (Jennings, 2001); other skills are needed. Quantitative skills need to be supplemented by skills in and knowledge of qualitative methods, such as, “in-depth interviews, semi-structured interviews, participant observation, focus groups, longitudinal studies, Delphic method, case studies, action research methods and documentary research” (Jennings, 2001, p. 378). Again, whilst most tourism economics and management researchers are skilled in descriptive and inferential statistics, there is a need for these researchers to develop skills in and knowledge of qualitative empirical materials, interpretation such as, “content analysis, constant comparative analysis, matrix building, mapping, successive approximation, domain analysis, taxonomy building, ideal type identification, eventstructure building and modelling” (Jennings, 2001, p. 378). As a result of travel and tourism researchers having knowledge and expertise in both quantitative and qualitative methodologies (and mixed methods):

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• the best match between research purpose and research design will be achieved; • researchers will be accountable for ensuring diversity in their research activities; and • researchers will be able to engage in critiques and reviews of research using paradigms outside of their sphere of familiarity (Jennings, 2003). Additionally, to address the criticism of tourism economics and management research for its mono-disciplinary and/or fragmented tourism enterprises using multidisciplinary rather than interdisciplinary approaches; and for tourism economics and management research to be more effective in the current and future world context, researchers need to develop and improve their competencies to be effective team players in interdisciplinary research projects. In regard to this capacity building, learning and training needs to incorporate team roles, crosscultural communication, and understanding of power dynamics. Moreover, researchers need to participate in research foci that cross local, regional, national and international ‘boundaries’. This is important as meanings of ‘space and place’ both contract and widen due to globalisation, internationalisation, and participation in the knowledge economy as well as in response to constant change. Tourism researchers require the aforementioned knowledge and skills if tourism researchers hope to remain relevant and accountable through their research activities in order to advance: • information for planning and management decision making, policy development; • understanding the social, environmental and economic impacts of tourism; • insights into motivations, needs, expectations and satisfaction levels of tourists; • identification of education and training requirements; • development of databases for use in comparative studies over time; • facilitation of industry requirements for product development; • monitoring and evaluating of tourism activities across the tourism industry and within stakeholder groups (Jennings, 2001, 2003). Moreover, tourism researchers need to choose theoretical paradigms from a suite of options rather than just the hegemonic paradigm. Tourism researchers also need to re-examine the continued replication

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and dominance of western-centric perspectives in theoretical, epistemological, methodological and axiological aspects of travel and tourism research projects. Such ethnocentric perspectives do not enable researchers to ‘truly’ take into account the experiences of those with different views, standpoints and experiences.

8 Conclusion In concluding this paper, I am again mindful of the writing of Miller (1976): “. . . dominant groups generally do not like to be told about or even quietly reminded of the existence of inequality. ‘Normally’ they can avoid awareness because their explanation of the relationship becomes so well integrated in other terms; they can even believe that they and the subordinate group share the same interests and, to some extent, a common experience . . .” (Miller, 1976, 6–8). That being said, it remains my belief that to advance tourism research and to be accountable in the twenty first century as well as to effectively account for tourism economics and management phenomena, travel and tourism research needs to draw on: • a suite of research paradigms; • a variety of epistemologies; • quantitative and qualitative methodologies and mixed methods approaches as well as indigenous methodologies; • interdisciplinary research projects (and multidisciplinary approaches); • team skilled projects (team roles, cross-cultural communication, power dynamics, trust and reciprocity); • studies in local, regional, and international spaces; • collaborations between communities, organizations, institutions and interested stakeholders both nationally and internationally; • multi-genre research outputs to serve a diversity of stakeholder audiences. In adopting such a research agenda, tourism economics and management research will provide a holistic and full account of the travel and tourism phenomenon under study within a glocal/global context in an ever changing world. Subsequently, this will serve to advance knowledge in tourism economics and management through tourism research

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which is accountable and cognisant of the varying ways that the world may be viewed paradigmatically through the specific consideration, incorporation and development of differing ontological, epistemological, methodological and axiological viewpoints.

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Jamal, T. and K. Hollinshead. (2001). Tourism and the forbidden zone: the underserved power of qualitative inquiry. Tourism Management, Vol. 22, pp. 63–82. Jamal, T. and J-H Lee. (2003). Integrating micro-macro approaches to tourist motivations: Toward an interdisciplinary theory. Tourism Analysis, Vol. 8, No. 1 pp. 47–59. Jennings, G.R. 2001. Tourism Research. Brisbane: John Wiley. Jennings, G.R. (2003). Tourism research: Theoretical paradigms and accountability. Targeted Research: the gateway to accountability: TTRA 34th Annual Conference Proceedings [CD Rom], June 15–18, St Louis, Missouri. Jennings, G.R. (2004) Business Research, Examples of Theoretical paradigms that inform, Encyclopedia of Social Measurement, San Diego, CA: Academic Press, pp. 211–217. Kemmis, S. and R. McTaggart. (1988). The action research planner, 3rd edition. Deakin University Press: Deakin University. Kemmis, S. and R. McTaggart. (2000). Participatory action research. In N.K. Denzin and Y.S. Lincoln, (Eds.). Handbook of qualitative research, 2nd edition. Thousand Oaks: Sage, pp. 567–605. Laclau, E. (1990). New reflections on the revolution of our time. London: Verso. Leiper, N. (1981). Towards a cohesive curriculum in tourism: the case for a distinct discipline. Annals of Tourism Research, Vol. 8, no. 1, pp. 69–74. Lewin, K. (1948). Action research and minority problems. In Lewin, K. (Ed.). Resolving social conflicts. New York: Harper and Row. Lincoln, Y.S. and E.G. Guba. (2000). Paradigmatic controversies, contradictions, and emerging confluences. In N.K. Denzin and Y.S. Lincoln, (Eds.). Handbook of qualitative research, 2nd edition. Thousand Oaks: Sage, pp. 163–188. Malhotra, N. (2001). Basic marketing research: application to contemporary issues with SPSS - Student edition. Prentice Hall.Source: http://www. superbookdeals. com /cgi - bin /moreinfo. cgi?page=desc&item=53577&bisac=Downloaded 05/08/03. Malhotra, N.K., Hall, J., Shaw, M. and P. Oppenheim. (2002). Marketing research: An Applied Orientation, 2nd edition. Frenchs Forest, NSW, Australia: Prentice Hall. Marcoulides, G.A. (1998). Modern Methods for Business Research. Mahwah, NJ: Lawrence Erlbaum Associates. McKercher, B. (1999). A chaos approach to tourism. Tourism Management, Vol. 20, No. 4, pp. 425–34. Miller, J.B. (1976). Towards a new psychology of women. Boston: Beacon Press. Moustakas, C. (1990). Heuristic research, design, methodology, and applications. Newbury Park: Sage. Naisbitt, J. (1982). Megatrends: Ten new directions transforming our lives. New York: Time Warner.

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Potter, G. and J. L´ opez. (2001). After postmodernism: the new millennium. In Potter, G. and J. L´ opez, (Eds.), After postmodernism: an introduction to critical realism. London: The Athlone Press, pp. 4–16. Przeclawski, K. (1993). Tourism as the subject of interdisciplinary research. In Pearce, D. G. & R.W. Butler, (Eds.). Tourism research, critiques and challenges. London: Routledge, pp. 9–13. Reason, Peter. (1994). Three approaches to participative inquiry. In N. K. Denzin and Y S. Lincoln, (Eds.). Handbook of Qualitative Research. Thousand Oaks: Sage, pp. 324–39. Remenyi, D., Williams, B., Money, A., and E. Swartz. (1998). Doing Research in Business and Management, An Introduction to Process and Method. London: Sage. Riley, R.W. and L.L. Love. (2000). The state of qualitative tourism research. Annals of Tourism Research, Vol. 27, No. 1, pp. 164–187. Ritchie, B.W., Burns, P. and C. Palmer. (2005). Tourism Research Methods: Integrating theory with practice. London: CABI. Ritchie, J.R.B. and C.R. Goeldner (Eds.). (1994). Travel, tourism and hospitality research, a handbook for managers and researchers, 2nd edition. New York: John Wiley. Robson, C. (2002). Real world research, a resource for social scientists and practitioner-researchers, 2nd edition. Oxford: Blackwell. Rubinstein, Moshe, F. and I.R. Firstenberg. (1999). The minding organization: Bringing the future to the present and turn creative ideas into business solutions. New York: John Wiley. Scheurich, J.J. (1997). Research methods in the postmodern. London: The Falmer Press. Schwandt, T.A. (2000). Three Epistemological Stances for Qualitative Inquiry: Interpretivism, Hermeneutics, and Social Constructionism. In N.K. Denzin and Y.S. Lincoln, (Eds.).Handbook of Qualitative Research, 2nd edition, Thousand Oaks, CA: Sage, pp. 189–213. Sekaran, U. (2003). Research Methods for Business, A Skill Building Approach. New York, NY: John Wiley and Sons. Sheldon, P.J. (1990). Journals in tourism and hospitality. Journal of Tourism Studies, Vol. 1, No. 1, pp. 42–48. Smith, S.L.J. (1995). Tourism analysis, 2nd edition. Harlow: Longman. Smith, L.T. (1999). Decolonizing methodologies – research and Indigenous peoples. London: Zed Books. Stanfield II, J.H. (1994). Ethnic modelling in qualitative research. In N.K. Denzin and Y.S. Lincoln (Eds.). Handbook of qualitative research. Newbury Park, CA: Sage, pp. 175–188). Stear, L. (1981). Design of a curriculum for destination studies. Annals of Tourism Research, Vol. 19, pp. 85–95. Tashakkori, A. and C. Teddlie. (1998). Mixed Methodology: Combining Qualitative and Quantitative Approaches. Applied Social Science Research Methods Series, Vol. 46. Sage, Thousand Oaks, CA.

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The Influence of Immigration and International Tourism on the Import Demand for Consumer Goods – A Theoretical Model Christian Fischer Institute for Food and Resource Economics, University of Bonn, Germany [email protected] or [email protected]

1 Introduction International migration and tourism (i.e. from the point of view of a single economy, inward and outward flows of people) have grown strongly during the last decades. In almost all OECD countries, immigrant levels have risen significantly during the period from 1986 to 2001. For example, while in Austria, Greece, the Netherlands, Sweden, the United States, Canada and New Zealand the percentage of foreignborn population in 2001 surpassed 10% of total population, Australia and Luxembourg each had already more than 20% (OECD, 2004). Growth in world tourism has been even stronger with almost 700 million worldwide tourist trips in 2000, as compared to about 25 million in 1950. Measured in relative terms, at 120 trips per thousand of world population in 2000, tourism activity has increased more than ten-fold during this period (World Tourism Organisation, various issues). At the same time, international merchandise trade has grown significantly during the last decades. However, while official statistics of the overall world trade of consumer goods1 (in contrast to industrial goods) 1

Statistics Canada (online) defines consumer goods as “new goods acquired by households for their own consumption. Comprise three categories: a) Durable goods, which can be used repeatedly or continuously for more than one year, such as motor vehicles and major appliances; b) Semi-durable goods, which can be used on multiple occasions and have an expected lifetime of one year or somewhat more, such as clothing, footwear and linens; c) Non-durable goods, which can be used only once, such as food, gasoline, alcoholic beverages and tobacco; in practice, the latter also include a few goods of little value used more than once, such as household supplies”.

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are unavailable, e.g. world food exports grew from current $221 billion in 1980 to $540 billion in 2003, representing 13.7% of world merchandise trade in that year (UNCTAD, 2005). Consumer-processed food and drink products made up more than 60% of world food trade in 1997 and was the segment which grew fastest between 1980 and 1997 (Gehlhar & Coyle, 2001). Thus, while there seems to be correlation between the flows of people and (certain) goods the question is whether there is also causality? International trade of good and services has been shown to be influenced by many factors of either push or pull character. Equally, the potential effects of migration and tourism on international trade can occur on both the supply and the demand side. That is, movements of people can contribute to generate exports, but they may also stimulate imports. Immigrants can bring specialised knowledge with them, such as business connections, language and cultural skills, which potentially contribute to lower transaction costs for the trade between the host and the source countries2 of the migrants and thus facilitate export development (Ethier, 1996). International tourism, on the other hand, can promote cross-border exports by initiating entrepreneurial activities as a result of learning about new business opportunities while travelling. There is a growing body of empirical studies, e.g. Aradhyula & Tronstad (2003), Shan & Wilson (2001), Kulendran & Wilson (2000), Easton (1998) on the effects of international travel, and Gould (1994), Rod & Webster (1995) or Aislabie et al. (1994) for those of immigration, which confirm the positive influence of immigration and international tourism on exports. However, the more direct links between movements of people and goods at the demand side have been analysed – theoretically as well as empirically – to a much lesser extent so far. First, immigrants may prefer consuming their home-country products while living in the host country, thus stimulating imports of certain goods. Second, demand for new products may be created as a consequence of learning about them during foreign travel. Empirical results confirming these hypotheses are scarce (see, however, Fischer (2004) for a 2

In the following, the terms source country and home country are used as synonyms, both representing the country from which the immigrant population/tourists originate. In contrast, the country where these people currently live (to which these people travel) is called host country. (In the literature, the terms “generating” and “receiving” country are also used instead of source and host country, respectively).

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quantification of these effects for food imports for the case of Germany), although statements that illustrate the connections can be found in the literature, e.g. RIRDC (1994, pp. 2–3). This chapter deals with the latter phenomenon only. It aims at analysing theoretically the potential of people movements to alter existing aggregate consumer preferences in favour of certain foreign consumer goods, thus leading to increased imports of these.3 Developing a preference for a product typically implies using existing information about the attributes of a product in order to compare these with an existing value structure. If these attributes are, or at least if they are believed to be, similar with our ideas of how a product should be, then we will develop a positive attitude (i.e. a preference) for it (Schaffner et al. 1998). Obtaining information about a product is a crucial first step for the development of a taste. For example, for foreign food products, information is typically transferred through migration, international travel and media (Carter, 1997; Gordon, 1998). The effect of media on the formation of aggregate preferences in general is difficult to measure and will not be investigated hereafter. While the existence of the promotional effect of immigration on the merchandise trade is widely acknowledged, its strength, however, may depend on the goods’ type. For example, Gould (1994, p. 303) argues that “Immigrant links to the home country have a strong positive impact on exports and imports, with the greatest effects on consumer manufactured exports. These effects tend to increase at a decreasing rate as the size of the immigrant community grows, and they also depend crucially on the type of goods traded”. As for international travel, Reed (1994) shows for example that tourism has a positive and significant effect on exports of processed food products but not on those of agricultural raw commodities and intermediate goods. This, too, suggests that the closer a product is to consumers, the stronger the effects of immigration and international tourism on its cross-country trade. The organisation of this chapter is as follows: first, after a discussion of some necessary assumptions, the connection between immigration and international tourism and the demand for consumer goods imported from the immigrants’ country of origin and/or the tourist destination country is analysed algebraically. Then, a geometrical model 3

In contrast to my article (Fischer, 2004), I will focus here on a generalised, and extended, theoretical analysis of the influence of immigration and international tourism on import demand.

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is presented. The last section summarises the findings and discusses avenues for further research.

2 Assumptions The following assumptions refer to exogenous conditions (i.e. those which are beyond anyone’s control) and serve as a platform on which the endogenous cause-effect relationships between the different levels of the model will occur. While these assumptions limit the universality of the proposed model, they define its context and thus determine its practical applicability. (1) In general, and ceteris paribus, it is assumed that immigrant tastes are biased against (at least some of) their source-country goods. That is, all other things being equal, they would prefer buying the imported goods over the locally-manufactured ones. (2) A population group must be prone to engage in trading. Some nationalities may like to engage more in small-scale trade businesses than others. (3) As for tourists, it is assumed that, at least some of them, develop a preference for foreign goods during their travel, and would be willing to buy these goods (i.e. to prefer them over their locally-produced ones) when back home. (4) Consumer goods, in particular culturally-bound ones such as certain food products, selected cloths, but perhaps also durable ones such as cars or television sets etc. are expected to be more likely to follow people flows than industrial goods such as raw commodities or intermediate products. (5) Goods may only be expected to follow people flows if people value these goods highly enough to compensate for the efforts, and the financial costs, that are needed to establish trade connections. That is, there must not be close substitutes for source-country goods available in the host country. (6) The source-country goods must be allowed to be imported into the host country – i.e. they must follow local safety rules and morality standards. Especially for food products this condition may not always be fulfilled, and trade may be blocked despite sufficient potential demand.4 4

Take raw milk cheese as an example: a product some types of which are not allowed to be imported into certain countries, such as the US or Australia, due to food safety concerns.

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3 Algebraic Model The connection between migration and tourism and trade flows of certain consumer goods can theoretically be established as follows. Demand qi for a consumer good i in a country is generally defined as a function of income I of the country’s consumers, the product’s own price pi , the prices of close substitutes (vector pS ), and consumer preferences z (Young & Burton, 1997). That is, qi = f(I, pi , pS , z) .

(1)

In traditional consumer theory and demand analysis, consumer preferences are usually not directly included in theoretical and empirical investigations due to difficulties in the identification and specification of appropriate indicator variables (von Alvensleben, 1997). However, for the analysis of aggregate foreign consumer good demand, immigration and tourism may be regarded as suitable instruments for revealing existing preferences, as it is shown in the following. Total demand Q for a certain consumer good in a particular host country h can be split first into the individual demands of different population groups – i.e. into demand for the good of the host population q H and demand for it of the foreign (or immigrant) population q F . Second, demand of both population groups can be further divided according to the origin of the goods: for host-country produced goods qp and imported consumer goods qm . Hence, total demand for consumer goods is equal to H F + qm . (2) Q = qpH + qpF + qm Import demand can be further differentiated according to the country n  qmj with n supplying of origin j of the consumer good – i.e. qm = j=0

countries. In the following, the focus is on the demand for a particular consumer good from one particular source country j (qmj ): T −J J + qm . qmj = qm j j

(3)

Equation (3) states that qmj is the sum of the demands for country’s j goods of the immigrant group J originating from this country and the rest of the host country’s population – i.e. the total population T minus J. It is assumed that T − J ≡ H. That is, all other immigrant groups

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are believed to behave similar to host population H. Equations (3) and (1) can now be combined to H J (I H , ph , pj , z H ) + qm (I J , ph , pj , z J ) , qmj = qm j j

(4)

which states that qmj depends on the incomes of the host, I H , and the immigrant population, I J ; the prices of host-country produced goods (including imports not originating from j: ph ), and goods, pj , imported from country j (seen here as close substitutes); and the – assumed as different – preferences of the host, z H , and the immigrant population, z J , originating from country j. Since the influence of income and prices on the demand for most basic consumer goods (such as food products) in industrialised societies, however, has diminished over the last decades (von Alvensleben, 1997), it may be justified to abstract from possible differences in incomes between the host and the immigrant population and price differences between locally- and foreign-manufactured goods, which in most cases at least can be assumed to be small. It can then be shown that qmj = f(S J ), with f  = ∂qmj /∂S J > 0 – i.e. consumer good imports from country j are an increasing function of the share of immigrants, S J , originating from that country in the total population of host country h. The overall share Aj of country j’s goods in country h’s total demand of these goods can be defined as qmj . (5) Aj = Q H qm

Let’s assume host-country inhabitants have a lower share αH = qHj of these goods consumed which are imported from country j than the J qm

one of immigrant population J originating from that country, αJ = qJj . That is, αH < αJ . Now, Aj can be calculated as Aj = αH ·S H +αJ ·S J , or since the share of the host population S H in total population equals 1 − SJ : (6) Aj = αH + (αJ − αH ) · S J . Given that αJ > αH , and assuming both shares are positive constants, it is therefore clear that Aj rises with higher immigrant shares in the host country and so does qmj , provided the total demand Q of the goods does not change. However, it may also be possible that the shares αJ and αH are not constant, but depend themselves on other factors.

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A positive relationship between the consumption habits of the host population and the level of immigration, such as αH = f(S J ), with f  = ∂αH /∂S J > 0, may be possible. It can be assumed that whenever an immigrant group brings with it its retail outlets, stalls on local produce markets, ethnic restaurants, etc., it will not only supply to its own ethnic group but the host population may also start to consume some of the new goods offered (see, e.g. K¨ohler, 1994). Therefore, the consumption habits of the host population may alter in the long run, provided that the ‘new’ goods offer some advantages (in terms of functionality, design, quality, taste, health perception, price, etc.) over the traditional, locally-produced goods. That is, this effect will enforce the positive relationship qmj = f(S J ), with f  = ∂qmj /∂S J > 0, between immigrant levels and good imports. In fact, it seems possible that also on the demand side the stimulating influence of immigration on trade flows diminishes with rising immigration levels (i.e. f  = ∂ 2 qmj / ∂S J 2 < 0), similar to the immigrant effect on export promotion as suggested by Gould (1994). Since it is likely that the change in consumption habits of the host population will only be partial, and once the interested host population has sufficient access to the newly imported goods, more immigrants will not bring further changes. However, more immigrants in general result in increased aggregate demand for a certain good, and assuming that they have a preference for their source-country goods, imports will rise proportionally to immigrant numbers. That is, f  = ∂ 2 qmj /∂S J 2 = 0. Thus, it depends on the size of the two individual effects whether their combined effect is actually decreasing or constant. Furthermore, it may also be possible that αJ changes over time t, since it can be assumed that the immigrant population adopts at least some of the local consumption habits, and thus prefers consuming the locally-produced goods. That is, αJ = f(t), with f  = ∂αJ /∂t < 0. Rising international tourism activity may have a similar potential to alter existing preferences in the host country population. Thus, imports of certain consumer goods can also be seen as a rising function of tourism, or qmj = f(T H ), with T being the share of country h tourists to country j in total country’s h population, and with f  = ∂ qmj /∂T H > 0. Very likely, this would only be true for part of the tourists and only for some countries and certain goods, but the effect might be strong enough to be measured at the aggregate market level. Thus, with rising international tourism to certain countries,

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country’s h aggregate preferences may be altered in favour of certain foreign consumer goods, resulting in a rise of imports of these. In summary, the import demand function of a country h for certain consumer goods originating from country j may be specified as +

+

−

+

+

+

qmj = qmj (I H , I J , pj , ph , S J , T H ) .

(7)

Import demand is assumed to increase in the incomes of host country’s population and country’s j immigrants and in prices for host-country produced goods, but to decrease in those for imported goods from country j. In addition, import demand increases in the share of country j’s immigrants in total host country’s population and in the share of country h’s tourists in its population travelling to country j. Depending on the good and the analysed country and immigrant group, the latter effects may be expected to be larger than those of the former variables.

4 Graphical Model In this section, the results which have been derived algebraically will be visualised in a two-goods, one-country graphical model. Thus, Fig. 1 provides an approach to illustrate the effects of rising immigrant levels and international travel of the host-country population to country j on consumer good imports from that country. The different CC  lines represent aggregate consumption possibility lines for the host country, immigrant and total population (superscripts H, J and T respectively). Those population groups are thought to face the choice for a certain consumer good of either manufactured at the host country h (ordinate) or imported from country j (abscissa).  C H 0 C H 0 describes the consumption choice of the host population. H can choose between either consuming only home-produced goods, goods imported from country j, or every combination of these two options. In the first case, the consumed (demanded) amount (qp ) would  be where C H 0 C H 0 intersects the ordinate. In the second case, the de manded amount (qmj ) would be were C H 0 C H 0 intersects the abscissa. Since we assume here that the tastes of the host population in general are biased against their locally-produced goods, a situation as described by qp H 0 and qmj H 0 seems most realistic.  C J 0 C J 0 describes the consumption choice of the immigrant population originating from source country j. Since the immigrant population

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45

Host country-produced goods (qp) CT1 CT0

qpT1 CH0

1

qpT0

0

qpH0

2

qpT2 CJ1 CJ0 qpJ1 qpJ0 0

CJ0′ qmjH0

qmjT0

qmjJ0

CJ1′ qmjJ1

CH0′ qmjT1 qmjT2

CT0′

CT1′

Goods imported from country j (qmj)

Fig. 1. The influence of immigration and international tourism on import

demand

is smaller is size than the host population and therefore the former’s  demand for the analysed consumer good is smaller, C J 0 C J 0 is more  H H to the left than C 0 C 0 . Assuming the immigrant group’s tastes are biased against the goods imported from j, a typical aggregate consumption preference may be described by qp J 0 and qmj J 0 .  C T 0 C T 0 depicts the total (aggregate) demand situation for the analysed good in the host country. It has been constructed by adding   C J 0 C J 0 and C H 0 C H 0 . Respectively, qp T 0 and qmj T 0 , which are the sums of qp H 0 and qp J 0 , qmj H 0 and qmj J 0 respectively, determine the current overall consumption preference in country h. The overall share Aj of country j’s goods in h’s total demand of qm these goods has been defined (see (5)) as Aj0 = Qj . Following the terminology of the graphical model, this is equivalent to

qmj T 0

0C0T 

.

With an increase of the immigrant population in country h, total population in general will grow too and the share of the immigrant group rises. A growing immigrant population implies a higher demand

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Christian Fischer 

for the analysed consumer good, resulting into a shift of C J 0 C J 0 towards the right. This new situation is depicted in Fig. 1 by the new  immigrant consumption possibility line C J 1 C J 1 . There is no change in  the host country population’s consumption possibility line C H 0 C H 0 , but the consumption possibility line of the total population would also  shift to the right, as it is shown in Fig. 1 with C T 1 C T 1 . As indicated in the figure by the move from point 0 to point 1, the increased total demand for the analysed good is mainly supplied through imports and only by little through locally-produced goods. Of course, the ratio depends on the preferences of the newly arrived population. In total, the new  share of imported goods in total demand is given by Aj1 = qmj T1 OC T  . Aj1 is larger than Aj0 as proven in (6). 1 The second effect of rising immigrant levels may be the change in consumption habits of the host population. As argued above, the imported consumer goods may be perceived by the host population (or at least by a part of it) as beneficial since these goods may offer, apart from simply signifying more variety in the overall spectrum of available goods, an advantage in terms of quality, design, taste, healthiness etc. over the locally-produced goods. Given the assumptions outlined before, the aggregate preferences in country h may then change in favour of the imported goods, which is illustrated in Fig. 1 by the shift from point 1 to point 2. This new  equilibrium is charT acterised by a much higher share Aj2 = qmj 2 OC T  (as compared to 1

Aj0 ) of imported goods in overall consumed goods due to the substitution of locally-manufactured goods by imported goods in the host population. Rising international tourism activity may have a similar potential to alter existing preferences in the host population. Tourists who learn about new goods during their travel may develop a taste for them and later on may want to consume these goods when back at home. That is, imports of foreign goods may also rise as a result of international travel. Of course, and as already stressed, this may only be true for a part of the tourists and only for some countries and some goods, however, the effect might be strong enough in order to be observed at the aggregate market level. The “mechanics” is in fact similar to the second effect of immigration described in Fig. 1. Thus, with rising international tourism to certain countries, the aggregate preferences may be altered in favour of foreign goods, resulting into increased imports of these.

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5 Conclusions This analysis has attempted to shed some light on demand side causes of international trade of consumer goods. The proposed model suggests that the movement of people, in the form of immigration and international tourism, may alter aggregate preferences in a country in favour of goods imported from an immigrant group’s source country or from the tourists receiving nation. This is so, because people who move gain access to information on new goods, and given a resulting new choice situation, people may prefer consuming those goods which they perceive as ‘better’. In this chapter it has, implicitly, been assumed that goods imported from country jare perceived as ‘better’, by both the immigrants originating from this country and the tourists travelling to j, resulting into increased imports of these goods. However, this may not be the case that both groups necessarily come to the same conclusion. In fact, the preferences of both population groups are probably completely independent from each other. It is of course also possible, that, e.g. immigrants learn about host country-produced goods and perceive them as superior to their source-country ones and start exporting them back home. In this case, immigration would promote exports. Also, it may be that tourists while being on their holidays would like to consume the goods from their home country (such as certain foods or media articles), and exports start to develop and follow tourist flows. Hence, in theory, everything is possible. The only way to find out which goods and which population groups tend to which particular behaviour would be possible by detailed empirical research. Empirical results for food products and the case of Germany (Fischer, 2004) show, e.g. that, for aggregate food imports from India and China, and for imports of wine, cheese and processed/preserved vegetables from France and Italy, it may be concluded that migration to Germany and international travel activities of Germans to these destinations have indeed contributed to rising food product imports from these countries. The tourism elasticities for individual food products have been found to lie between one and two in absolute terms. Also, consistent with a priori expectations, they are below unity for aggregate food imports from the analysed Asian countries. As expected, the estimates of the immigration elasticities are higher than those for international tourism activities. In some cases, as for the imports of drinking wine from France, and of cheese and processing wine from Italy, the immigration elasticities have been estimated as being well

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above two in absolute terms. Although these empirical results seem to confirm theoretical considerations, there is scope for more work. Further research would use a more systematic empirical approach, starting with a large database of imports of different consumer goods, and would attempt to cluster these goods according to their association with international tourism and immigration flows. Then this analysis would look for similar patterns in different countries in order to arrive at conclusions of whether some population groups are more likely to engage in activities that make their source countries’ goods available in their current host countries, or whether tourists from some countries are more likely to buy goods imported from their favourite holiday destinations. Also, it would be possible to identify those goods whose trade flows generally are more likely to be affected by the flows of people. Such extended research results would be useful for both international marketing professionals and public policy makers. Export managers of consumer good companies would have effective information about possible crucial target groups for their market entry strategies. Regional development officials may learn that, e.g. promoting tourism into an area could prove to be an effective means for enhancing the export success of local manufacturing industries.

References Aislabie, C., Lee, J. & Stanton, J. (1994). Australian Cultural Diversity and Export Growth. Canberra: Australian Office of Multicultural Affairs. Alvensleben, von R. (1997). Consumer Behaviour. In: Padberg, D.I., Ritson, C. & Albisu, L.M. (eds). Agro-Food Marketing. New York: CAB International, pp. 209–224. Aradhyula, S. & Tronstad, R. (2003). Does Tourism Promote Cross-Border Trade? American Journal of Agricultural Economics. Vol. 85, No. 3, pp. 569–579. Carter, S. (1997). Global Agricultural Marketing Management. Rome: FAO. Easton, S.T. (1998). Is Tourism Just Another Commodity? Links between Commodity Trade and Tourism. Journal of Economic Integration. Vol. 13, pp. 522–543. Ethier, W.J. (1996). Theories About Trade Liberalisation and Migration: Substitutes or Complements? In: Lloyd, P.J. & Williams, L.S. (eds). International Trade and Migration in the APEC Region. Melbourne: Oxford University Press, pp. 50–68.

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Fischer, C. (2004). The influence of immigration and international tourism on the demand for imported food products. Food Economics. Vol. 1, No. 1, pp. 21–33. Gehlhar, M., & Coyle, W. (2001). Global Food Consumption and Impact on Trade Patterns. In Regmi, A. (ed.). Changing Structure of Global Food Consumption and Trade. Washington DC: Economic Research Service (ERS), US Department of Agriculture, pp. 4–13. Gordon, A.D. (1998). Changes in Food and Drink Consumption, and the Implication for Food Marketing. In: OECD (ed). The Future of Food – Long-Term Prospects for the Agro-Food Sector. Paris: OECD, pp. 91–110. Gould, D.M. (1994). Immigrant Links to the Home Country: Empirical Implications for U.S. Bilateral Trade Flows. Review of Economics and Statistics. Vol. 76, pp. 302–16. Kulendran, N. & Wilson, K. (2000). Is there a relationship between international trade and international travel? Applied Economics. Vol. 32, pp. 1001–1009. K¨ ohler, S. (1994). Kulturelle Vielfalt in der Ern¨ ahrung – Die zunehmende Bedeutung ausl¨andischer Kost in der BR Deutschland. Agrarwirtschaft. Vol. 42, pp. 328–336. OECD (Organisation for Economic Co-operation and Development) (2004). Trends in International Migration. Paris: OECD. Reed, M.R. (1994). Importance of Non-price Factors to Competitiveness in International Food Trade. In: Bredahl, M.E., Abbott, P.C. & Reed, M.R. (eds). Competitiveness in International Food Markets. Boulder: Westview Press, pp. 83–102. RIRDC (Rural Industries Research and Development Corporation) (1994). Asian Food – Getting a Bigger Bite. Canberra: National Capital Printing. Rod, T. & Webster, E. (1995). Immigration and Trade with East Asia in the Food Industry – The Contribution of Immigrants. Canberra: Bureau of Immigration, Multicultural and Population Research. Schaffner, D.J., Schroder, W.R. & Earle, M.D. (1998). Food Marketing – An International Perspective. Boston: McGraw-Hill, Inc. Shan, J. & Wilson, K. (2001). Causality Between Trade and Tourism: Empirical Evidence from China. Applied Economic Letters. Vol. 8, pp. 279–283. Statistics Canada (October 2004). Statistical methods/Glossary. Online accessed: http://www.statcan.ca/english/concepts/chainfisher/ glossary.htm#c UNCTAD (United Nations Conference on Trade and Development) (2005). Handbook of Statistics. Geneva: UNCTAD. World Travel Organisation (various issues). World Travel Statistics. Geneva: WTO. Young, T. & Burton, M. (1997). Supply and Demand of Agricultural Products. In: Padberg, D.I., Ritson, C. & Albisu, L.M. (eds). Agro-Food Marketing. New York: CAB International, pp. 29–50.

An Economic Analysis of Tourism Contracts: Allotment and Free Sale∗ Massimiliano Castellani1 and Maurizio Mussoni2 1

2

University of Bologna, Department of Economics, Piazza Scaravilli 2, 40125 Bologna, Italy [email protected] Universit` a degli Studi di Bologna, Alma Mater Studiorum, Facolt` a di Economia-Sede di Rimini, Via Angher` a, 22, 47900 Rimini, Italy [email protected] or [email protected]

1 Introduction In the tourism sector it is very difficult to find “complete contracts”. The typically incomplete tourism contracts regulating the business relationship between Tour Operators and Hotels are Free Sale and Allotment contracts. In this work we analyze these incomplete contracts from a microeconomic perspective and within an “optimal contract design” approach.3 In particular through “optimal contract design” and “incomplete contract” approaches it is possible to analyze the design and efficiency consequences of imperfections resulting from contractual incompleteness.4 We shall compare the observed stylized facts (the actual business relationships and the contracts) with the results from our theoretical models, in order to verify that the economic analysis applied to contracts can be used to look for solutions to some of the typical tourism economic problems. Three questions are specifically addressed within our setting: (i) how do Tour Operators and Hotels choose the optimal contract terms (quantity and price), given the initial conditions; ∗ 3

4

We would like to thank C. Benassi and G. Candela for helpful comments on a previous version of the paper. The usual disclaimer applies. For more details on “optimal contract design”, see Shavell S. (1984), Hart O.D. and B. Holmstrom (1987), Holmstrom B. and J. Tirole (1989), Werin L. and H. Wijkander (1992), Tirole J. (1994), Masten S.E. (1988) and (1999). See Holmstrom B. and J. Tirole (1989), Tirole J. (1994), Masten S.E. (1999).

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(ii) which is the “optimal risk sharing” rule for the Allotment contract; (iii) which is the “optimal risk allocation” rule for the Free Sale contract? The work is structured as follows: in Sect. 2 we present the main stylized facts of the tourism sector and the assumptions for our model. Next, in Sect. 3 we analyze Free Sale and Allotment contracts within a framework of “law and economics”, while in Sect. 4 we set up the models of the two contracts (Allotment and Free Sale) and we obtain their analytical solutions. Then, in Sect. 5 we show the results of a numerical simulation of the two models. Finally, a comparative analysis between the two models in terms of “optimal contract design” concludes our work.

2 Stylized Facts and Assumptions There are some stylized facts in the tourism sector that are very remarkable when the contractual relationship between Tour Operators and Hotels has to be analyzed. One peculiarity of the tourism sector is that the market demand is subject to seasonality and uncertainty. The tourism market demand is stochastic because it depends on stochastic and exogenous variables, e.g. natural resources, institutional aspects, social and cultural elements, economic and political conditions, psychological influence. Further uncertainty is due to the time that passes from the “purchase” (booking) to the “consumption” of a tourism-industry product (time consuming). Additionally, goods in this industry are typical experience goods and the consumption is time intensive. For these reasons in this work we assume that demand, reservation price, frequency and holidays duration are all stochastic variables. Another stylized fact in the tourism sector is that the tourism product is a non-storable good.5 In fact, the final product supplied to the customer is not a physical good, but an immaterial service-good. For this reason – and given the seasonality and uncertainty of market demand – Tour Operators and Hotels are subject to many economic risks in carrying out their activity.6 5 6

For example, an empty hotel room is a service which is “produced” but not sold. It should be stressed that we are speaking of “economic risks” and not of “technical risks”, related for example to the safety of person, transport or baggages. In these latter cases, nothing is different from the normal insurance contracts.

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Since Tour Operators are exposed to an economic risk due to the risk of market demand changes and Hotels are subject to the economic risk due to the possibility that some rooms remain empty (unsold), Free Sale and Allotment contracts can be of help in managing these risks.7 Therefore in this work we assume that Tour Operators operate only in the future market, while Hotels can sell their services (free rooms) both to Tour Operators on the futures market and directly to tourists on the spot market.8 For some tourism firms we observe the existence of a high degree of monopoly power. In fact, often both Tour Operators and Hotels act in market structures which can be described by monopolistic competition or oligopoly models. Hotels tourism services are extremely differentiated with respect to the location, to the typology of customers, to the typology and quality of services supplied, to the facility dimension and the ownership. It is therefore reasonable to assume a high degree of monopoly power. Since the main service supplied by Tour Operators in the “package tour” is the Hotel accommodation, the analogous market structure can be assumed for Tour Operators. For this reason it is reasonable to assume that the method used by Tour Operators to charge the supply price (or “posted price”) is based on mark-up percentage.9 Another feature of the tourism market is that the “tourism facility”10 is characterized by a certain degree of rigidity. This rigidity means that the facility dimension can be changed by the firms only in the long run through investment decisions. For the purpose of this research, it is sufficient to analyze the firm’s decisions only in the short run, without taking into account fixed costs imputation and investment decisions.11 7 8

9 10

11

Another way to manage the risk is the “overbooking” technique. See Candela G. and P. Figini (2005). In fact, the available rooms can be sold by Hotels either (i) on the future market, i.e. to Tour Operators through the Free Sale or the Allotment contract (indirect distribution), or (ii) on the spot market, i.e. directly to consumers-tourists (direct distribution). If the quantity of rooms booked in advance by Tour Operators is not sufficient to satisfy the market demand, then Hotels have the possibility to sell these additional rooms demanded by the tourists at the spot market price. In our model we assume this percentage as an exogenous parameter. For Hotels the “tourist facility” consists of the number of available rooms (capacity), while for Tour Operators of the maximum number of persons for whom the “package tour” is planned. In our model, we will formalize this Hotels supply rigidity through the introduction of a capacity constraint.

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In tourism markets we observe two main typologies of consumertourists: (i) “independent tourists”, who buy single tourism services directly from tourism firms; (ii) “common tourists”, who usually buy bundled tourism services (“package tours”) in advance on the future market only from Tour Operators (or Travel Agencies), while they buy them directly from Hotels only on the spot market.12 In our model we study only the second typology of consumer-tourists and the corresponding tourism market, i.e. we assume that the future market of tourism services is open only to the Tour Operators. These issues correspond to five important assumptions of our model, as we shall formally present in Sect. 4: (i) a monopoly power, often existing both for Tour Operators and Hotels13 ; (ii) a stochastic tourism market demand and thereafter a framework of choices under uncertainty; (iii) tourism products are non-storable services; (iv) a short run temporal horizon for firm’s decisions and accordingly a capacity constraint for Hotels; (v) future market is closed to the consumers-tourists.

3 Free Sale and Allotment Contracts Free Sale and Allotment contracts are accommodation contracts, in which the exchange consists of “cash for rooms”, where the Hotel rooms are filled-in at a later stage Therefore, they are contingent and future contracts, in which the main problem is to overcome the negative effects of uncertainty caused by deferred exchanges.14 Such contracts are proposed by Tour Operators to Hotels in order to share (Allotment contract) or to cover (Free Sale contract) the economic risk due to a potential low or high market demand (unsold or overbooking rooms), that could limit the realization of the exchange. The exchange and the level 12

13 14

In fact, since Tour Operators sign in advance the agreements with their suppliers, they are then capable to manage accommodation and transportation services and moreover they can guarantee a certain level of safety, quality and price for the holiday. It should be stressed that most of the phenomena analyzed in this paper could be studied even in perfect or imperfect competition. See Tirole J. (1988). See Masten S.E. (1999), p. 29: “Sophisticated but boundedly rational transactors will omit contingencies when the costs of anticipating, devising optimal responses to, and drafting provisions for improbable events outweigh the expected gains in efficiency from doing so. Departures from the Arrow-Debreu ideal may thus arise in incomplete contract theory from failures of the contracting parties to foresee and provide for contingencies in formulating their agreement, instead of or in addition to the inability of courts to verify performance.”

An Economic Analysis of Tourism Contracts

55

of the two main variables involved, i.e. price and quantity of booked rooms, depend therefore on the following aspects: the two agents riskaversion degrees; the probability they assign to the different stochastic events (high or low market demand) and their bargaining power. In particular, the Free Sale contract can be regarded as a sort of insurance contract regulating a “risk-transfer” transaction, through which Tour Operators assume all of the economic risk (insurance level) by booking a fixed number of rooms (contractual quantity) in advance. Hotels then give a discount (insurance premium), in return, on the booking price (contractual price). Through this contract the Tour Operator purchases the agreed number of rooms (accommodations) related to a certain limited period of time and commits itself to pay the entire agreed price before the actual demand is known, i.e. independent from the future actual room occupancy. For this reason this contract is known in the literature as a “fixed quantity commitment” contract15 and the reservation of a fixed number of rooms in advance leaves Tour Operators with a high risk due to market demand uncertainty. On the contrary, the Allotment contract is a typical “risk-sharing” agreement, which represents a mechanism of sharing the risk between both parties and is known in the literature as a “quantity flexibility” contract.16 In this case, a number of rooms (contractual quantity) is available for sale by Tour Operators for a limited period of time, and Tour Operators commit themselves to confirm the actual room occupancy to the Hotels within an agreed deadline (the “release date”). Tour Operators pay for this right to confirm or not confirm the actual room occupancy with an “option exercise price” which is equal to a percentage of the accommodation value (contractual price). Tour Operators exercise this option (called “call option”) at the release date (European call), or within the release date (American call), by confirming or releasing the rooms. On the contrary, if the confirmation of room occupancy is not exercised within the agreed deadline or in accordance with the contractual clauses, Hotels can sell the empty rooms directly to tourists on the spot market. According to economic theory, we could say that both Free Sale and Allotment contracts belong to the following typologies of contracts: (i) contingent contracts and (ii) future contracts. The main difference 15 16

See Anupindi R. and Y. Bassok (1998), and Araman V.F., J. Kleinknecht and R. Akella (2003). See Li C. and P. Kouvelis (1999), Tsay A. (1999) and Barnes-Schuster D., Y. Bassok and R. Anupindi (2000).

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between them is that the Free Sale contract is an insurance contract, or a risk-transfer transaction, whose function is to shift the economic risk to the least risk-averse agent, while the Allotment contract is an options contract, or a risk-sharing agreement, whose function is to share the economic risk between the two risk-averse agents.17

4 The Models From the “stylized facts” of the tourism sector and the economic features of the two accommodation contracts described in the previous Sections, we derive the economic insights that allow us to build our models of the two contracts. Let us present some common characteristics of the two models. Both for Hotels and Tour Operators, the risk is increased by the seasonality and uncertainty of market demand. It is therefore very important for these firms to foresee market demand, in order to maximize firm profit. The two economic decisions to be taken are: (i) the number of rooms (for Hotels) and thereby the number of “package tours” (for Tour Operators) to be sold on the market; (ii) how to charge the selling price. Therefore in our models the two decision variables are, respectively, the contractual quantity (number of rooms booked in advance by Tour Operators up to the Hotel capacity constraint) and the contractual price (purchase price for Tour Operators which includes a discount given by Hotels or an option price paid to Hotels with respect to the spot market price). On the contrary, we shall assume as exogenous parameters the other economic variables involved (in particular, Tour Operator mark-up and Hotel production costs). In Sect. 2 we have presented the topic assumptions of a seasonal and uncertain market demand and of a monopoly market both for Tour Operators and Hotels. In our models, the market is therefore composed of two monopolistic tourism firms, the Tour Operator and the Hotel, and the consumers-tourists. Now we add the further assumption that the two firms have symmetric information such that they can have the same expectations about future market demand.18 Usually the different “states of nature”, on which stochastic demand is conditioned, represent a continuous set of events but here, for 17 18

See Cheung Steven N.S. (1969) and Stiglitz J. (1974). This is a reasonable assumption since they can have access to the same information regarding the future behavior of market demand.

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57

simplicity, we consider only a discrete case. Let us therefore assume the states of nature are only two: D L (low market demand) and D H (high market demand)19 and the corresponding probabilities assigned both by the Tour Operator and the Hotel are equal to αL and αH . This implies that the tourism market demand is a random variable:   D = D L ; DH with probabilities αL and αH , (1) where αL + αH = 1 (therefore αL = 1 − αH ) . 4.1 The Allotment Model The choice of the optimal decision under conditions of uncertainty depends substantially on the risk attitude. According to this point of view, the basic assumption that justifies the existence of the Allotment contract is that both Tour Operators and Hotels are risk-averse agents. In fact, the occurrence of two risk-averse agents is a necessary condition for a risk-sharing agreement, but not a sufficient condition for a Pareto Optimal contract in a framework of “choices under uncertainty”. We can specify the Allotment contract clauses in the following way. The contractual price is different depending on the Tour Operator’s choice to confirm, and therefore to buy, or to release the rooms. In these two cases the Tour Operator will pay: P a = P + o for confirmed rooms and P a = o for released rooms (or P a = P for confirmed rooms and P a = o for all booked rooms), where P a is the “contractual price”.20 While P represents the given price on the spot market, 0 ≤ o ≤ 1 is the option price paid by the Tour Operator to the Hotel, and E is Hotel supply, i.e. the “contractual quantity” (rooms booked in advance). We assume the following capacity constraint for the Hotel21 : E ≤ K where K is the maximum number of rooms that the Hotel can supply 19

20 21

Note that a stochastic market demand could be introduced in the model by assuming “price uncertainty”, instead of “quantity uncertainty”. The case with “price uncertainty” is anyway symmetric with respect to our model, while the more complex case in which both uncertainties are assumed and analyzed simultaneously could represent an interesting improvement of this model. To insure the economic significance of the price P a , we introduce the positivity constraint (positive price condition) P a = P + o ≥ 0 and therefore P ≥ −o. We recall from Sect. 2 that the assumption of a short run temporal horizon for firm’s decisions implies the introduction of a capacity constraint for the Hotel. Moreover, it implies the choice of not taking into account fixed costs imputation and investment decisions.

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in the short run.22 Therefore the available rooms can be sold by the Hotel: either on the future market, i.e. to the Tour Operator through the Allotment contract (indirect distribution) or on the spot market, i.e. directly to the consumers-tourists (direct distribution). In this way the Tour Operator and the Hotel share the risk of unsold rooms, through the mechanism of an option price o paid by the Tour Operator to the Hotel. If the rooms E booked in advance are partially released by the Tour Operator, or are all confirmed but still not sufficient to satisfy market demand, then the Hotel has the possibility to sell these additional rooms directly to the tourists at the spot market price P . From the Allotment contractual clauses, we observe that the option price o paid by the Tour Operator for the right to confirm or release the actual rooms (which in our model is the endogenous component of “contractual price” P a ) is equal to a percentage of the accommodation value. The two decision variables of the Allotment model, for which we want to find out an optimal decision rule, are: E, number of rooms booked in advance by the Tour Operator (contractual quantity) and o, unit option price paid by the Tour Operator to the Hotel (implicit contractual price). Once the Tour Operator has decided to confirm the rooms, it will include them in the “package tours” and it will sell them, usually through the intermediation of a Travel Agency, at the future price (called “posted price” or “brochure price”): P s = P (1 + m) where m > 0 is the mark-up percentage applied by the Tour Operator, that we assume in our model as an exogenous parameter.23 For simplicity we normalize the spot market price P ≡ 1, so that the contractual prices become24 : P a = 1 + o for confirmed rooms and P a = o for released rooms (or P a = 1 for confirmed rooms and P a = o for all booked rooms) and P s = 1 + m. It must be stressed that a “positive profit” condition for the Tour Operator which is the natural consequence of the assumption of a monopoly market, implies that P s > P a , condition respected if m > o. 22 23 24

For simplicity, we assume that all the rooms are standard rooms, e.g. double rooms. The Tour Operator does not charge the mark-up on the full cost P a , but on the spot market price P . As previously stressed, the economic significance of the price P a implies the positivity constraint P a = 1 + o ≥ 0, and therefore 1 ≥ −o, always true by definition. On the contrary, the positivity of the price P s is implicit in the assumption m > 0.

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59

In this way, we are also assuming that the Tour Operator is applying a “fixed price” strategy: its products (“package tours”) are sold at a unique price that does not change in the period of time considered.25 Similarly we can assume a “positive profit” condition also for the Hotel, that implies that P a > c (where c > 0 is the Hotel unit production cost) and therefore, after price normalization, that o > c. To simplify the analysis, we assume the following condition on the quantity variable: (2) DL ≤ E ≤ DH . In fact, since the Tour Operator knows that market demand will be D L or DH , it would not be economically rational to book: (i) less than D L rooms (E < DL ), otherwise the Tour Operator would loose   L certainly s the favourable condition of an amount equal to P D − E ; (ii) more than D H rooms (E > DH ), otherwise the Tour Operator would have a certain loss due to the option price to be paid for the reservations in  H excess (released rooms), for an amount equal to o E − D . The basic assumption of a stochastic market demand implies that Tour Operator sales are stochastic too.26 We therefore assume that the Tour Operator sales S are a random variable conditional on observed demand, according to the so called “short-side rule”27 : S = min [D, E] which is the “min” condition in the canonical disequilibrium model when the observed amount of transaction is equal to the minimum between the supply and the demand. The “short-side rule” assumption is consistent with the fact that the fixed selling price P s (“fixed price” strategy) is higher than the booking price P a (“positive profit” condition). From these assumptions it follows that for the Tour Operator, it is profitable to sell (to confirm) every room demanded by the tourists, up to the quantity of booked rooms, otherwise (i) if D = D L , by selling (confirming) S L < DL the Tour Operator would have an opportunity 25

26 27

On the basis of this “fixed price” strategy, the selling prices could change only in a different season or for extraordinary reasons. This is actually one of the price strategies that are most used by the touristic firms, while other possible price strategies are: changing price depending on the quality of the service (vertical differentiation) or on the different characteristics of demand segments (price discrimination). We assume that the rooms sold by the Tour Operator are identically equal to the rooms confirmed within the release date. According to the “short-side rule”, or “Hahn-Negishi rule”, the short side of the market determines the transaction quantity. See Hahn F.H. and T. Negishi (1962), Quandt R. (1988) and Fisher F.M. (1983).

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  cost equal to P s DL − S L with probability αL ; (ii) if D = DH , by selling (confirming) S H < E the Tour Operator would have a certain loss due the  option price to beHpaid for the released rooms, equal  to H with probability α . Another important consequence to o E − S of these assumptions is that intuitively we expect that supposing an exogenous increase in the probability of high market demand αH , the optimal quantity of rooms E ∗ will always be higher. Another way to define the “short-side rule”, is to express the Tour Operator sales as the following random variable:  L S = DL with probability αL = 1 − αH (3) S= S H = E with probability αH To summarize, the model has the following assumptions: (i) a market composed of two monopolistic firms (the Tour Operator and the Hotel) and of consumers-tourists, (ii) a stochastic tourism demand, (iii) the form of an Allotment contract is analogous to an options contract, (iv) risk-aversion of the two agents and (v) exogenous market parameters (spot market price, Tour Operator mark-up, Hotel unit cost). Through this model we want to find the optimal contract in terms of (i) rooms E booked in advance by the Tour Operator, and (ii) option price o paid to the Hotel. To model the Allotment contract, we start from the definition of Tour Operator and Hotel utility functions. Since the Tour Operator is an expected utility maximizer with a Bernoulli utility function over its profit28 , we can define its utility function as29 : UT O (E, o) = Ex [uT O (ΠT O (E, o))] , where Ex is “expected value” and ΠT O is the Tour Operator profit, which we define as: ΠT O (E, o) = P s S − P a E. The Tour Operator utility becomes:   UT O (E, o) = αL uT O P (1 + m)S L − P S L − oE +  ,  (4) +αH uT O P (1 + m)S H − P S H − oE 28

29

For further elements about risk-neutrality and risk-aversion, linear and concave utility functions and expected utility maximizers with a Bernoulli utility function, see Mas-Colell A., M.D. Whinston and J.R. Green (1995). Because of the concavity of a risk-averse agent utility function, we assume that  u > 0 and u ≤ 0. Furthermore, we assume that uo < 0 and uE > 0.

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and applying the “short-side rule” (3) and taking into account that αL = 1 − αH , we have the final result30 :     UT O (E, o) = 1 − αH uT O mDL − oE + αH uT O [mE − oE] . (5) Since the Tour Operator maximizes its expected profit subject to the Hotel participation constraint, we define Hotel utility function over its profit as follows31 : Uh (E, o) = Ex [uh (Πh (E, o))]. We define the Hotel profit as: Πh (E, o) = P a S − cE, where c > 0 is the Hotel unit production cost.32 This profit takes a different form depending on the actual market demand, because in the case of low market demand (D = D L ) it becomes: a L ΠL h (E, o) = P S − cE ,

while in the case of high market demand (D = D H ) it becomes   H  a H H . ΠH h (E, o) = P S − cE + P − c D − S The reason for this difference is that only with high market demand the quantity of rooms confirmed by the Tour Operator (S = S H ) is not sufficient to satisfy the entire market demand.33 Therefore the Hotel utility becomes equal to:   Uh (E, o) = αL uh P S L + (o − c) E +     (6) +αH uh P S H + (o − c) E + P − c DH − S H and applying the “short-side rule” (3) and taking into account that αL = 1 − αH we have:       Uh (E, o) = 1 − αH uh DL + (o − c) E + αH uh (1 − c) DH + oE . (7) 30

31 32 33

Similarly to the “positive profit” condition, a “positive expected profit”    condition for the Tour Operator implies that 1 − αH uT O mDL − oE + αH uT O [mE − oE] > 0, condition as well respected if m > 0. Because of the concavity of a risk-averse agent utility function, we assume that  u > 0 and u ≤ 0. Furthermore, we assume that uo > 0 and uE > 0. We assume therefore the following Hotel production cost: C (E) = cE. The Hotel can sell these additional rooms on the market (which are in excess with respect to the rooms confirmed by the Tour Operator) at  directly   to the tourists  the spot market price P , for an amount equal to P − c DH − S H . As we have previously seen, the Hotel could sell directly to the tourists also the rooms released by the Tour Operator but in this case the all market demand is satisfied by the Tour Operator and accordingly the Hotel can sell the rooms only to the Tour Operator.

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The Hotel participation constraint takes the form Uh (E, o) ≥ U h , where U h is the Hotel reservation utility, which is an exogenous variable equal to the expected profit deriving from sales on the spot market:      U h = Ex uh P − c min K, D . (8) Finally, we have the following optimization problem in which the Tour Operator’s goal is to achieve the maximum expected profit, subject to Hotel participation and capacity constraints: M ax UT O (E, o) = Ex [uT O (ΠT O (E, o))] E, o

s.t. Uh (E, o) = Ex [uh (Πh (E, o))] ≥ U h and E ≤ K ,

(9)

where Uh (E, o) ≥ U h is the participation constraint and E ≤ K is the capacity constraint. Since we do not consider capacity constraint E ≤ K 34 , from the Lagrangian First Order Conditions (FOC’s) we obtain:    1 − αH uTLO + αH uH Ex (uT O ) TO ∗   , = (10) λ = H H Ex uh (1 − αH ) uL h + α uh where uT O = 

∂uT O ∂ΠT O ,



uTLO =

∂uT O , ∂ΠTLO



uTHO =

∂uT O , ∂ΠTHO

uh =

∂uh ∂Πh ,



uhL =

∂uh ∂ΠhL

∂uh and uhH = ∂Π H. h According to the second FOC we obtain, by plugging in λ = λ∗ , the following equation: 

o [Ex (uT O )] − mαH uTHO Ex (uT O )    = λ∗ = −    .  Ex uh o Ex uh − c (1 − αH ) uhL

(11)

Finally, the third FOC is:       1 − αH uh DL + (o − c) E + αH uh (1 − c) DH + oE − U h = 0 . (12) 34

We can make the following qualitative analysis: (i) if K > DH , the constraint is not binding; (ii) if K ≥ E ∗ ⇒ E = E ∗ , the constraint is not binding; (iii) if K|! < E ∗ ⇒ E = K, the constraint is binding and the Hotel can not entirely fulfill the contract proposed by the Tour Operator. For the participation constraint Uh (E, o) ≥ U h we assume that the Lagrangian multiplier is . > 0. It is economically rational to consider only the hypothesis of strictly equal Uh (E, o) = U h (and therefore . > 0) rather than the hypothesis of strictly greater Uh (E, o) > U h (and therefore . = 0) because in this way we minimize the Tour Operator costs, Hotel participation being equal. Hence, we obtain a higher maximum expected profit, which implies a higher maximum utility.

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From the second and third FOC’s, we derive the “efficient contract” in terms of the two decision variables E ∗ and o∗ (conditional on exogenous parameters D L , DH , U h , αH , m, c), but this is not a Pareto Optimal Solution because the Tour Operator marginal rate of substitution is not equal to the Hotel marginal rate of substitution.35 On the contrary, from (11) and from the Pareto Optimal Condition, we derive the necessary (but not sufficient) condition for a Pareto Optimal contract: M RST O = M RSh =

m , c



where M RST O =

(1−αH ) uTLO 

αH uTHO

(13) 

and M RSh =

(1−αH ) uhL 

αH uhH

are the Tour

Operator and the Hotel marginal rates of substitution between their marginal utilities in the case of low and high profit. This condition is called “optimal risk sharing” rule because it represents the condition under which a socially efficient risk sharing takes place since it is optimal for the two firms to insure each other. This rule can be expressed as follows: 



uhL uTLO αH m . = = · H H H 1−α c uT O uh

(14)

Starting with (14) it is possible to analyze the absence of aggregate uncertainty in the tourism market: 



uhL uTLO = =1.   uTHO uhH

(15)

In this equation the “optimal risk sharing” rule for the two firms implies the choice to fully insure each other, such that there is indifference    between the two possible states of nature: since uTLO = uTHO and uhL = H H L H uh then ΠL T O = ΠT O and Πh = Πh . In our model, the condition under which it is optimal for the two firms to take out a reciprocal full insurance is the following one: αH = 35

c , m+c

(16)

Note that the condition for the Pareto Optimality of a risk allocation between two risk-averse agents (in our model the Tour Operator and the Hotel) is the equivalence between their marginal rates of substitution: M RST O = M RSh . See McKenna C.J. (1986).

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c where it can be shown that αH = m+c ≤ 1 since m > 0 by assumption. Starting with this condition and recalling that 0 ≤ αH ≤ 1, we have the following “qualitative” conclusions about the Allotment contract and c H then ΠL the “optimal risk sharing” rule: (i) only if αH = m+c T O = ΠT O H and ΠL h = Πh , i.e. there is no aggregate uncertainty in the market, therefore it is optimal for the Tour Operator to propose an Allotment contract with a reciprocal full coverage to the Hotel; (ii) only if αH = c L H L H m+c then ΠT O = ΠT O and Πh = Πh , i.e. there is aggregate uncertainty in the market, therefore it is optimal for the Tour Operator to propose an Allotment contract with a reciprocal partial coverage36 to the Hotel. In conclusion, we have shown that the Allotment contract is an options contract which is not completely Pareto Optimal partially due to the assumption of two risk-averse agents. Our model shows that under “optimal risk sharing” rule (14) an efficient contract can lead to Pareto Optimality depending on the value of αH and of the parameters m and c. Normally we only find the conditions for a sub-optimal (but efficient) contract.

4.2 The Free Sale Model The most important difference between Allotment and Free Sale contracts derives from the different risk-aversion degree of the agents. While the existence of the Allotment contract is justified by the assumption that both Tour Operators and Hotels are risk-averse agents, behind the Free Sale contract is the following basic assumption: the Tour Operator is a risk-neutral agent37 while the Hotel is a risk-averse agent.38 The first assumption can be justified because the Tour Operator often is a firm large enough to manage the risk without going bankrupt. The costs will be thereby widely spread over the company’s customers, shareholders and others. The assumption is reasonable especially when the firm has many shareholders, therefore the owners are capable of 36

37 38

c In particular, because of utility functions concavity (i) only if αH > m+c then L H L H H L c ΠT O < ΠT O and Πh < Πh and (ii) only if α < m+c then ΠT O > ΠH T O and L H Πh > Πh . Formally, this implies that the Tour Operator has a linear utility function, i.e. an utility function that exhibits a constant marginal utility. Formally, this implies that the Hotel has a concave utility function, i.e. an utility function that exhibits a diminishing marginal utility.

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diversifying the risk via the financial markets.39 In fact, the occurrence of two parties one of which is risk-averse and the other risk-neutral, is a necessary condition for an insurance market, but it is not a sufficient condition for a Pareto Optimal contract in a framework of “choices under uncertainty”.40 The other obvious difference between Allotment and Free Sale contracts consists in their different contractual clauses. According to the Free Sale contract the Hotel sells rooms in advance to the Tour Operator at the price: P c = P − d, where P c is the “contractual price”41 , 0 ≤ d ≤ 1 is the discount (insurance premium) granted by the Hotel to the Tour Operator and E is Hotel supply, i.e. the “contractual quantity” (rooms booked in advance). In this way the Hotel insures itself against the risk of unsold rooms, granting the discount-premium d to the Tour Operator in return. In the case that quantity E of rooms booked in advance by the Tour Operator is not sufficient to satisfy market demand, then the Hotel has the possibility to sell these additional rooms directly to the tourists at the higher spot market price P . In terms of the Free Sale contract clauses, the discount d is equal to a percentage of the accommodation value. Therefore, the Free Sale model decision variables are: E, number of rooms booked in advance by the Tour Operator (contractual quantity) and d, discount rate given by the Hotel to the Tour Operator (implicit contractual price). Once the Tour Operator has booked the rooms, it will include them in the “package tours” and it will sell them at a future selling price (usually through the intermediation of a Travel Agency) P s = P (1 + m) .42 Once again we normalize the spot market price P ≡ 1, so that the contractual prices become43 : P c = 1 − d and P s = 1 + m. 39 40 41

42 43

For a definition of risk-aversion and risk-neutrality, see Cooter R. and T. Ulen (2004) and Faure M. and S. Goran (2003). See McKenna C.J. (1986) and Rossini G. (1994). To insure the economic significance of the price P c , we introduce the following positivity constraint (positive price condition): P c = P − d ≥ 0 and therefore 0 ≤ d ≤ P. The Tour Operator does not charge the mark-up on the full cost P c , but on the spot market price P . As previously stressed, the economic significance of the price P c implies the following positivity constraint: P c = 1 − d ≥ 0 and therefore 0 ≤ d ≤ 1, always true by definition. The positivity of the price P s is instead implicit in the assumption m > 0.

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Tour Operator “positive profit” condition P s > P c is respected if m > −d (always true because d ≥ 0 by assumption). The Hotel “positive profit” condition P c > c is respected if 0 < c < 1 (given that d ≥ 0 by assumption). Again we assume condition (2) on the quantity variable E, because it would not be economically rational to book: (i) less than D L rooms (E < DL ), otherwise the Tour Operator would certainly loose   favourable conditions, for an amount equal to P s DL − E ; (ii) more than D H rooms (E > DH ), otherwise the Tour Operator would have a certain loss for therooms purchased in excess (unsold rooms), for an  amount equal to P c E − DH . We always assume that Tour Operator sales S are a random variable conditional on observed demand, according to the “short-side rule” (3). For the Tour Operator it is therefore profitable to sell every room on the market, up to total booked rooms, otherwise: (i) if D = D L , by selling S L < DL the Tour Operator would have an opportunity cost s equal to P DL − S L with probability αL ; (ii) if D = DH , by selling would have a certain loss for unsold rooms, S H < E theTour Operator  equal to P c E − S H with probability αH . To summarize, the Free Sale model differs from Allotment model only in the following assumptions: (i) the Tour Operator is risk-neutral and the Hotel is risk-averse and (ii) the form of a Free Sale contract is analogous to an insurance contract. We want to find the “optimal contract” in terms of: (i) rooms booked in advance by the Tour Operator (E, insurance level); (ii) discount given by the Hotel (d, insurance premium). Given the assumption of a risk-neutral Tour Operator, we can define its linear utility function as44 : UT O (E, d) = Ex [ΠT O (E, d)]. Since the c E, its expected value d) = P s S − P Tour Operator profit is: ΠT O (E,   is: Ex [P s S − P c E] = (1 + m) S L αL + S H αH − (E − dE). According to the “short-side rule” (3) and taking into account that L α = 1 − αH , we have45 :   (17) UT O (E, d) = (1 + m) DL − DL αH + EαH − (E − dE) .

44 45

We assume that ud > 0 and uE > 0. Similarly to the “positive profit” condition,a “positive expectedprofit” condition for the Tour Operator implies that (1 + m) DL − DL aH + EaH −(E − dE) > 0, condition as well respected if m > −d.

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Given the assumption of a risk-averse Hotel, its utility function over its profit can be defined as46 : Uh (E, d) = Ex [uh (Πh (E, d))]. Since Hotel profit is: Πh (E, d) = (P c − c) E, where c > 0 is the Hotel unit production cost, the Hotel utility is47 :     Uh (E, d) = 1 − αH uh [(1 − d − c) E] + αH uh −dE + (1 − c) DH . (18) ¯h , where U h is The Hotel participation constraint is Uh (E, d) ≥ U the Hotel reservation utility (8). Therefore the optimization problem becomes: M ax UT O (E, d) = Ex [ΠT O (E, d)] E, d

s.t.Uh (E, d) = Ex [uh (Πh (E, d))] ≥ U h and E ≤ K ,

(19)

where Uh (E, d) ≥ U h is the participation constraint and E ≤ K is the capacity constraint. As far as capacity constraint E ≤ K and participation constraint Uh (E, d) ≥ U h are concerned, we can make a qualitative analysis analogous to the one already made for the Allotment model. Therefore, from the Lagrangian First Order Conditions (FOC’s) we obtain: λ∗ =

1 1   . =   L H (1 − αH ) uh + αH uh Ex uh

(20)

According to the second FOC we obtain the following equation by plugging in λ = λ∗ : M RSh = 46 47

1 − (1 + m) αH , (1 + m) αH − c

(21)

Because of the concavity of a risk-averse agent utility function, we assume that u > 0 and u ≤ 0. Furthermore, we assume that ud < 0 and uE > 0. The Hotel profit takes a different form depending on the actual market demand. The reason, as we have already seen for the Allotment contract, is that only in the case of a high market demand (D = DH ) the quantity of rooms booked in advance by the Tour Operator (E) can be not sufficient to satisfy entirely the market demand. The Hotel can therefore sell these additional rooms on the market (which are in excess with respect to the contractual commitment of the Tour Operator) tothe tourists at the spot market price P¯ , for an amount  directly  equal to P¯ − c DH − E .

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Massimiliano Castellani and Maurizio Mussoni 

where M RSh =

(1−αH ) uhL  αH uhH

is the Hotel marginal rate of substitution

between the Hotel marginal utilities in the case of low and high profit. Finally, the third FOC is:     1 − αH uh [(1 − d − c) E] + αH uh −dE + (1 − c) DH − U h = 0 . (22) From the second and third FOC’s, we derive the “efficient contract” in terms of the two decision variables E ∗ and d∗ (conditional on the exogenous parameters D H , U h , αH , m, c), but this is not a Pareto Optimal Solution because the Hotel marginal rate of substitution (21) is not equal to one.48 On the other hand, from (21) and from Pareto Optimal Condition, we derive the necessary (but not sufficient) condition for a Pareto Optimal contract: (23) M RSh = 1 that can be expressed as follows: 

uhL αH 1 − (1 + m) αH =1. · H = 1 − αH (1 + m) αH − c uh

(24)

This condition is called the “optimal risk allocation” rule, i.e. it is the condition under which a socially efficient risk allocation takes place, because it is optimal for the Tour Operator to fully insure the Hotel.49 This rule can be expressed as follows: αH =

c . m+c

(25)

The “optimal risk allocation” rule implies therefore that there is indifference between the two possible states of nature and Hotel profits   become deterministic: since uhL = uhH then ΠhL = ΠhH . 48

Note that the condition for the Pareto Optimality of a risk allocation between a risk-neutral agent (in our model the Tour Operator) and a risk-averse 

agent (in our model the Hotel) is the following one: M RSh = 49

uhL

 uhH

= 1. See

McKenna C.J. (1986). In this way we have a Pareto Optimal risk allocation because the risk is totally shifted to the least risk-averse agent.

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Therefore, in the Free Sale model the condition under which it is optimal for the Hotel to take out full insurance is (25) where it can be c ≤ 1 since m > 0 by assumption. Starting with shown that αH = m+c this condition and recalling that 0 ≤ αH ≤ 1, we have the following “qualitative” conclusions about the Free Sale contract: (i) full insurance c (then ΠhL = ΠhH )50 ; (ii) partial insurance only if only if αH = m+c c c αH < m+c (then ΠhL < ΠhH )51 ; (iii) no insurance only if αH > m+c (then ΠhL > ΠhH ).52

5 A Numerical Simulation of the Models Now we present a numerical simulation of Allotment and Free Sale models in order to investigate how the optimal choice about decision variables (quantity and price) changes if we consider a change in the exogenous parameters (comparative statics). 5.1 The Allotment Model Let us begin the simulation of the Allotment model from the following system composed by the above stated (11) and (12):

⎧      o (1−αH ) uTLO +αH uTHO −mαH uTHO ⎨ (1−αH ) uTLO +αH uTHO =−      (1−αH ) uhL +αH uhH o[(1−αH ) uhL +αH uhH ]−c(1−αH ) uhL      ⎩ 1 − αH uh DL + (o − c) E + αH uh (1 − c) DH + oE = U h (26) 50

51

52

In this case it is optimal for the Tour Operator to take out full insurance (“optimal risk allocation rule”). Therefore, we have a Pareto Optimal risk allocation because the risk is totally shifted to the least risk-averse agent. In this case it is optimal for the Tour Operator to take out only partial insurance. Therefore, we have a Pareto Sub-optimality (or socially inefficient risk allocation) because the risk is not totally borne by the risk-neutral agent. Furthermore this result confirms the intuition for which a higher discount given by the Hotel corresponds to a higher number of rooms booked by the Tour Operator (and vice versa) and it’s consistent with the stylized fact that usually in the tourism market the discount rate is positively correlated with the quantity exchanged. In this case there are not the conditions to take out insurance at all because under optimistic expectations about market forecasts it is optimal for the Tour Operator not to propose any contract at all to the Hotel (case of non-insurance). Therefore, this case implies the greatest social inefficiency because the risk-averse agent faces the total risk.

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To perform a numerical simulation of this system of two equations in two unknowns (E, o), first of all it is necessary to specify the form of both Hotel utility function uh and Tour Operator utility function uT O . Since they are both risk-averse by assumption, we specify their utility functions through a natural logarithmic function: Uh (E, o) = Ex [ln (Πh (E, o))] and UT O (E, o) = Ex [ln (ΠT O (E, o))] . In this way, the system (26) after some algebraic manipulations becomes:      1−αH 1−αH 1−αH αH αH αH c 

+ (m−o) E (1−c) D L +oE = m (1−c) D L +oE + (1−c) D H +oE mD L −oE      1 − αH ln D L + (o − c) E + αH ln (1 − c) D H + oE = U h

(m−o) E

,

(27)   H where U h = (1 − α) ln (1 − + α ln (1 − c) D . Now, if we consider the two extreme cases in which the problem becomes a deterministic one, we have the following results: 

c) DL



H • if  α = 1, then the system becomes: m = 0  ln (1 − c) DH + oE − U = 0 H • if  α = 0, then the system becomes: c = 0  ln DL + (o − c) E − U = 0.

From these results we derive that in a “choice under certainty” framework, in which the market demand is deterministic, the Hotel becomes totally indifferent between the choice to sell rooms on the future market (i.e. to the Tour Operator through the Allotment contract) or on the spot market (i.e. directly to the consumers-tourists), therefore the Allotment contract becomes economically “useless”. We want moreover to observe how the equilibrium values of the two decision variables E ∗ (optimal quantity of rooms booked by the Tour Operator) and o∗ (optimal option price paid by the Tour Operator to the Hotel) change if we suppose a change in the exogenous parameter αH (probability of high market demand): in this way, we can analyze the effects on the equilibrium values of an exogenous change in market demand, giventhe market supply. To do that, we

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71

0.2 m = 0.15 m = 0.2 m = 0.25 m = 0.30

0 –0.2 –0.4

o

–0.6 –0.8 –1 –1.2 –1.4 –1.6

0.3

0.4

0.5 α

0.6

0.7

Fig. 1. Equilibrium values of the option price (o∗ ) for different values of the

probability of high market demand (αH ) and the Tour Operator mark-up (m)

fix the following starting numerical values for the remaining exogenous parameters: DL = 25; DH = 50; K = 100; c = 0.35 and we obtain the following graphs, which plot αH against o∗ (Fig. 1) and against E ∗ (Fig. 2). These results confirm the intuition for which if we suppose an exogenous increase in the probability of high market demand αH , the optimal quantity of rooms E ∗ will always be higher (since the Tour Operator foresees a higher future market demand and therefore higher sales53 ) as well as the optimal option price o∗ .54 From this simulation 53

54

This is the consequence of the assumption that the fixed selling price P s (“fixed price” strategy) is higher than the booking price P a (“positive profit” condition) and therefore that for the Tour Operator it is profitable to sell (to confirm) every room demanded by the market, up to the quantity of booked rooms E. This result is consistent with the canonical fact that the supply curve has a positive slope while the demand curve has a negative slope. In this way, if we suppose an exogenous increase in the market demand, coeteris paribus, both the price and the quantity exchanged in equilibrium will be higher.

72

Massimiliano Castellani and Maurizio Mussoni 70 m = 0.15 m = 0.2 m = 0.25 m = 0.30

60

50

E

40

30

20

10

0

0.3

0.4

0.5

0.6

0.7

α

Fig. 2. Equilibrium values of the rooms booked by the Tour Operator (E ∗ )

for different values of the probability of high market demand (αH ) and the Tour Operator mark-up (m)

we therefore always observe a positive correlation between the equilibrium values of the decision variables E ∗ and o∗ , which is consistent with the stylized fact that usually a higher option price corresponds to a higher number of booked rooms (and vice versa).55 Finally, we fix the probability of high market demand at the value H α = 0.5, i.e. we consider the case of perfect uncertainty, and we observe how the equilibrium values of the two decision variables E ∗ and o∗ change for different values of the exogenous parameters m (Tour Operator mark-up) and of c (Hotel marginal cost). In this way we obtain the following graphs, which plot c and m against o∗ (Fig. 3) and against E ∗ (Fig. 4). 55

Moreover, from the graphs we observe that both the lines describing the behavior of equilibrium values of the endogenous variables E ∗ and o∗ (which are depicted for some different values of the exogenous parameter m) present some “kinks” at some values of αH . We deduce that these points may correspond to the different threshold values of αH extracted from the analytical solution of the Allotment model which yields the corresponding “qualitative” conclusions about the Free Sale contract (cases of reciprocal full insurance or reciprocal partial insurance).

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0.4

o

0.3 0.2 0.1 0

1

–0.1 0.5

0.8 0.6

0.4 0.4

0.3 0.2

0.2 c

0.1

0

m

Fig. 3. Equilibrium values of the option price (o∗ ) for different values of the

Hotel marginal cost (c) and the Tour Operator mark-up (m)

From this simulation we observe that if Tour Operator mark-up m increases, then in equilibrium it happens that: • the option price o increases56 , i.e. intuitively if the Tour Operator has more bargaining power (its degree of monopoly power is higher) it becomes more profitable to rise its final selling price P s and it can therefore afford to pay a higher option price to the Hotel57 ; • the number of booked rooms E increases58 , consistently with the previous result, i.e. for the Tour Operator it becomes more profitable 56

57

58

Note that in Fig. 3 the left and light region of the graph lies in the negative area, therefore it does not respect the positivity constraint which guarantees the economic significance of the variables and accordingly it must not be evaluated. In other words, Tour Operator profit will be higher with the strategy “high price P s -high option price o” rather than with the strategy “low price P s -low option price o.” Note that in Fig. 4 the left and dark region of the graph must not be evaluated, because it lies below the area depicted by the numerical values that we fixed for the two possible states of nature DL = 25 (low market demand) and DH = 50 (high market demand).

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Massimiliano Castellani and Maurizio Mussoni

250 200

E

150 100 50 0 0.5 0.4 0.8

0.3

0.6

0.2

0.4 0.1

c

1

0.2 0

m

Fig. 4. Equilibrium values of the rooms booked by the Tour Operator (E ∗ )

for different values of the Hotel marginal cost (c) and the Tour Operator mark-up (m)

to rise its final selling price P s and therefore to supply, and hence to book, a higher number of rooms.59 If instead the Hotel marginal cost c increases, then in equilibrium it happens that: • the option price o increases, i.e. if the Hotel has higher costs, it can accept only a contract with a higher option price paid by the Tour Operator; • the number of booked rooms E decreases, consistently with the previous result, i.e. since its purchase price P a will rise, for the Tour Operator it becomes more profitable to demand, and hence to book, a lower number of rooms.60 59

60

Since we are analyzing a canonical supply relationship between price and quantity, this result is consistent with the stylized facts that usually the supply curve has a positive slope. Since we are analyzing a canonical demand relationship between price and quantity, this result is consistent with the stylized facts that usually the demand curve has a negative slope.

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5.2 The Free Sale Model Now we simulate the Free Sale model starting with the system composed by the above stated (21) and (22): ⎧  ⎨ (1−αH ) uhL = 1−(1+m) αH  (1+m) αH −c αH uhH ⎩1 − αH  u [(1 − d − c) E] + αH u −dE + DH (1 − c) = U . h h h (28) Like for the Allotment model, to perform a numerical simulation of this system of two equations in two unknowns (E, d), it is necessary to specify the form of the two firms utility functions, but in this case we need to specify only the Hotel utility function uh because the Tour Operator utility function uT O has already been specified with a linear utility function.61 Given the assumption of a risk-averse Hotel, we specify its utility over its profit through a natural logarithmic function: Uh (E, d) = Ex [ln (Πh (E, d))] . So the system (28) becomes: ⎧ ⎨ (1−αH ) [−dE+DH (1−c)] αH = 1−(1+m) αH E(1−d−c) (1+m) αH −c ⎩1 − αH  ln [E (1 − d − c)] + αH ln −dE + DH (1 − c) = U . h (29) If we consider the two extreme cases (αH = 1 and αH = 0) we have analogous results, so that also the Free Sale contract becomes economically “useless” in a “choice under certainty” framework: 

m = 0  ln −dE + DH (1 − −c) − U = 0  c=0 = 0, then the system becomes: ln [E (1 − d − c)] − U = 0

• if α = 1, then the system becomes H

• if αH

Now again we observe how the equilibrium values of the two decision variables E ∗ (optimal quantity of rooms) and d∗ (optimal discount rate) change if we suppose a change in the exogenous parameter αH (probability of high market demand), i.e. we analyze the effects on the equilibrium values of an exogenous change in market demand, given the market supply. As before, we fix the usual starting numerical values for 61

Recall that given the assumption of a risk-neutral Tour Operator , we can define its linear utility function as UT O (E, d) = Ex [ΠT O (E, d)].

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Massimiliano Castellani and Maurizio Mussoni

the remaining exogenous parameters (D L = 25; DH = 50; K = 100; c = 0.35) and we obtain the following graphs, which plot αH against d∗ (Fig. 5) and against E ∗ (Fig. 6). Again these results confirm the intuition for which if we suppose an exogenous increase in the probability of high market demand αH , the optimal quantity of rooms E ∗ will always be higher, but in this case the optimal discount d∗ will be higher only until a certain threshold value of αH and afterwards on the contrary it will be lower. From this simulation we therefore observe the following correlation between the equilibrium values of the decision variables E ∗ and d∗ : • if αH ≤ 0.65 then there is a positive correlation among E ∗ and d∗ , i.e. below a certain threshold value of the probability of high demand αH , it is necessary a higher discount rate to sell more rooms; this result, analogously to the Allotment model simulation, is consistent with the stylized fact that usually a higher discount corresponds to a higher number of booked rooms (and vice versa);

m = 0.15 m = 0.2 m = 0.25 m = 0.30

0.25

d

0.2

0.15

0.1

0.05

0 0.5

0.55

0.6

α

0.65

0.7

0.75

Fig. 5. Equilibrium values of the discount rate (d∗ ) for different values of the

probability of high market demand (αH ) and the Tour Operator mark-up (m)

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77

250 m = 0.15 m = 0.2 m = 0.25 m = 0.30

200

150 E 100

50

0 0.25

0.3

0.35

0.4

0.45

0.5

0.55

0.6

0.65

0.7

0.75

α

Fig. 6. Equilibrium values of the rooms booked by the Tour Operator (E ∗ )

for different values of the probability of high market demand (αH ) and the Tour Operator mark-up (m)

• if αH ≥ 0.65 then there is a negative correlation among E ∗ and d∗ , i.e. above a certain threshold value of the probability of high demand αH , it is sufficient a lower discount rate to sell more rooms; intuitively, under optimistic expectations about market demand for the Hotel it is profitable to give a lower incentive to the Tour Operator by decreasing the discount rate. These results confirm the analytical solution of the Free Sale model, c ∼ according to which there exists a threshold value αH = m+c = 0.65 which yields the corresponding “qualitative” conclusions about the Free Sale contract (cases of full insurance, partial insurance or noninsurance). Finally, like in the previous Allotment model simulation, we fix the probability of high market demand at the value αH = 0.5 (therefore αH < 0.65), i.e. the case of perfect uncertainty and partial insurance, and we observe how the equilibrium values of the two decision variables E ∗ and d∗ change for different values of the exogenous parameters m

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Massimiliano Castellani and Maurizio Mussoni

(Tour Operator mark-up) and of c (Hotel marginal cost). In this way we obtain the following graphs, which plot c and m against d∗ (Fig. 7) and against E ∗ (Fig. 8). From this simulation we observe that if Tour Operator mark-up m increases, then in equilibrium it happens that: • the discount rate d increases for low values of m (m ≤ 0.5)62 , while d decreases for high values of m (m ≥ 0.5)63 ;

0.5 0.4 0.3 d

0.2 0.1 0 1

–0.1 0.5

0.8 0.4

0.6 0.4 0.2 c

0.1

0.2

m

0

Fig. 7. Equilibrium values of the discount rate (d∗ ) for different values of the

Hotel marginal cost (c) and the Tour Operator mark-up (m) 62

63

Intuitively, if the Tour Operator degree of monopoly power increases, but anyway its mark-up is low in absolute terms, it becomes more profitable to reduce its final selling price P s and it can therefore afford only a higher discount given by the Hotel. In other words, Tour Operator profit will be higher with the strategy “low price P s (high E)-high discount rate d” rather than with the strategy “high price P s (low E)-low discount rate d”. This result is also due to the assumption that the Tour Operator does not charge the mark-up on full costs but fully gets the discount given by the Hotel. Therefore in this case the Tour Operator prefers to reduce its final selling price to increase both the number of sold rooms and the total discount received from the Hotel. Intuitively, if the degree of monopoly power of the Tour Operator is very high, for the Tour Operator it becomes more profitable to rise its final selling price P s and it can therefore afford a lower discount given by the Hotel. In other words,

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250 200 150 E 100 50 0 0.5 0.4

1 0.8

0.3

0.6

0.2 c

0.4 0.2

0.1 0

m

Fig. 8. Equilibrium values of the rooms booked by the Tour Operator (E ∗ )

for different values of the Hotel marginal cost (c) and the Tour Operator mark-up (m)

• the number of booked rooms E increases64 , i.e. for the Tour Operator it becomes more profitable to rise its final selling price P s and therefore to supply, and hence to book, a higher number of rooms. If instead the Hotel marginal cost c increases, then in equilibrium it happens that: • the discount rate d decreases, i.e. if the Hotel has higher costs, it can accept only a contract with a lower discount for the Tour Operator; • the number of booked rooms E decreases65 , consistently with the previous result, i.e. since its purchase price P a will rise, for the Tour

64 65

the Tour Operator profit will be higher with the strategy “high price P s (low E)low discount rate d” rather than with the strategy “low price P s (high E)-high discount rate d”. Therefore in this particular case the Tour Operator prefers to rise its final selling price even at the expense of a lower number of sold rooms. Once again, this result is consistent with the stylized facts that usually the supply curve has a positive slope. Once again, this last result is consistent with the stylized facts that usually the demand curve has a negative slope.

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Operator it becomes more profitable to demand, and hence to book, a lower number of rooms.66

6 Conclusions In this work our intent was to model Allotment and Free Sale contracts in order to compare and analyze them in terms of their efficiency and Pareto Optimality. From the analytical solution of the two models we have shown that the exchange and the level of the two contractual variables, the price (o for the Allotment contract and d for the Free Sale contract) and the number of booked rooms (E for both the contracts), depend mainly on three factors: (i) the degree of risk-aversion for the two agents; (ii) the probability they assign to the different stochastic events (high or low market demand); and (iii) their bargaining power. We have seen that the two contracts are consistent with the main stylized facts observable in the tourism market. The Allotment contract takes the form of an options contract (risk-sharing agreement) between two risk-averse agents, while the Free Sale contract takes the form of an insurance contract (risk-transfer transaction) between a risk-neutral agent and a risk-averse agent. Through a comparative analysis in terms of contract efficiency, we can affirm that both contracts are not completely Pareto Optimal because the solutions obtained from the models are individually efficient (i.e. we have an “efficient contract”) but not socially efficient or Pareto Optimal (i.e. we do not have an “optimal contract”). In fact, the general solutions obtained from the two models do not satisfy the corresponding conditions for the Pareto Optimality in a framework of risk allocation between two risk-averse agents (for the Allotment contract) or between a risk-neutral agent and a risk-averse agent (for the Free Sale contract). Therefore, both cases lead to a sub-optimal contract, though efficient for the agents. 66

From the Hotel standpoint, it becomes more profitable to sell to the Tour Operator a lower number of rooms compared to the opposite choice. Intuitively, this result is correct only if, marginal costs being equal, the Hotel expected profit in the case of low market demand (“low sales”) is higher than the expected profit in the case of high market demand (“high sales”), given the lower total discount d granted in the case of “low sales”. In other words, it can be shown that Hotel profit will be higher with the strategy “low sales-low discount” rather than with the strategy “high sales-high discount” only if the total discount dE dominates the total cost cE.

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Nevertheless, the Pareto Optimal Solutions, i.e. “optimal risk sharing” rule (13) and “optimal risk allocation” rule (23), depend on: (i) the probability αH (or, analogously, αL = 1 − αH ) assigned to the future market demand and (ii) the value of the exogenous parameters m (Tour Operator mark-up) and c (Hotel unit cost). In fact, both types of contracts share the same Pareto Optimal Condition for a full insurance c . (rather than a partial insurance): αH <=> m+c c H Precisely, only when α = m+c , does (i) M RST O = M RSh = 1 meaning the Allotment contract is optimal, therefore it is optimal both for the Tour Operator and the Hotel to fully insure each other and (ii) M RSh = 1 meaning the Free Sale contract is optimal, therefore it is optimal for the Tour Operator to fully insure the Hotel. c , then On the contrary, for both types of contracts, when αH = m+c we only have the conditions for the optimality of a partial insurance. c in the Free Sale contract, the contract is no Moreover, if αH > m+c longer profitable for the Tour Operator. This result is especially important because it shows that the two contracts have a common threshold value (16 and 25) which is independent from the specification of the utility functions for the two agents, i.e. from their different risk-aversion degrees, and independent from both the different legal characteristics and natures of the contracts. Starting with the optimal decision rules we have shown, through a numerical simulation, how the “optimal contract”67 changes if we consider a change in the exogenous parameters. The numerical simulation of the Allotment model shows the following results: • there is a positive correlation between the equilibrium values of the decision variables E ∗ and o∗ , which is consistent with the stylized fact that a higher option price usually corresponds to a higher number of booked rooms (and vice versa); • an increase of Tour Operator mark-up m leads to an increase of the option price o and of the number of booked rooms E 68 ; this result shows how a change in Tour Operator degree of monopoly power can influence the equilibrium (in a framework of imperfect competition market); 67

68

We recall that the optimal contract is given by the equilibrium values of the decision variables E (number of booked rooms) and o (option price), for the Allotment contract, or d (discount rate), for the Free Sale contract. This result is due to the fact that for the Tour Operator it becomes more profitable to rise its final selling price P s and therefore to supply, and hence to book, a higher number of rooms.

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• an increase of the Hotel marginal cost c leads to an increase of the option price o and to a decrease of the number of booked rooms E; this result shows the importance of cost analysis. The numerical simulation of the Free Sale model shows the following results: • confirmation that if the probability of high demand αH is below c a certain minimum threshold (only if αH < m+c ) or if the probability of low demand is above a certain maximum threshold, then the equilibrium values of the discount rate d and of the number of booked rooms E are positively correlated. Therefore, there are c , then favourable conditions for partial insurance (only if αH = m+c 69 a full coverage insurance contract for the Hotel is feasible) ; • an increase of Tour Operator mark-up m leads to an increase of the number of booked rooms E; this result shows how the market power can influence the demand choices of the Tour Operator; • an increase of the Hotel marginal cost c leads to a decrease of the discount rate d and of the number of booked rooms E; this result shows how the cost parameter can influence the supply choices of the Hotel.70 Our conclusion argues that there are several potential causes for the contracts not being completely Pareto Optimal. In both cases the reason of this Pareto Sub-optimality can be found in the legal specifications of the contracts (i.e. terms and clauses) or in the economic specification of our models. In our opinion, these factors could not be sufficient incentive for the agents to behave optimally. In the Allotment contract one possible cause could be the specification of option price o, while in the Free Sale contract it could be the specification of discount rate d. Moreover, this inefficiency may result from the two different objectives mixed into Allotment 69

70

This result confirms the stylized fact that if the Tour Operator books a higher number of rooms it can obtain a higher discount from the Hotel (and vice versa), and it shows that the realization of the exchange depends on the probability of the favorable event αH or, analogously, on the probability of the adverse event c then the possibility of insuring a risk-averse αL = 1 − αH (because if αH > m+c Hotel is not even proposed by the Tour Operator). In fact, from the Hotel standpoint we have seen that the strategy to sell to the Tour Operator a lower number of rooms and to give a lower discount, is more profitable only in the case the total discount granted dominates the total cost sustained. In other words, an efficient Hotel (i.e. with lower marginal production costs) can obtain better contractual conditions.

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and Free Sale contracts, i.e. profit maximization and risk allocation. As a consequence, it may be possible to modify the contracts in order to limit, or remove, their inefficiencies.71 Another assumption that could mitigate the two contracts inefficiencies is the possibility for the Tour Operator to negotiate with more than one Hotel.72 This strategy would allow the Tour Operator to both diversify the risk and eliminate the single Hotel capacity constraint. Analyzing the effect of the different degrees of risk-aversion for the agents73 or of the different trends in Tour Operator mark-up74 on the optimal contractual choices (in particular the choice between full and partial insurance) could also lead to different results. Furthermore, it would be interesting to model the case in which Tour Operators and Hotels agree on a contract which is a mix between Free Sale and Allotment contractual clauses. In conclusion we would like to suggest other possible extensions to our models, such as introducing a certain degree of competition among the Tour Operators75 , assuming a simultaneous uncertainty in prices (market price) and quantities (market demand)76 , analyzing the possibility for the Tour Operator to follow a strategy of vertical integration.77 or applying the “mean-variance model”.78

References Anupindi R. and Y. Bassok (1998). Supply Contracts with Quantity Commitments and Stochastic Demand, Quantitative Models for Supply Chain Management, Tayur S., M. Magazine and R. Ganshan (eds.), Kluwer Academic Publishers, Boston, MA. 71

72 73 74 75 76 77

78

One possible revision could be, for instance, to change the specification of the option price o or of the discount rate d, such that they are function both of booked rooms E and of probability αH , i.e. o(E, αH ) and d(E, αH ). In this way, we could relax the assumption of a monopoly market for the Hotels. A big Hotel, e.g. could be a firm more capable to manage the risk than a little Hotel. In fact, usually regarding the mark-up we observe a procyclical trend. By relaxing the assumption of a monopoly market for Tour Operators, the Hotel could look for alternative Tour Operators. This assumption implies two uncertainty sources for the firms: price risk and demand risk. Through this strategy, the Tour Operator becomes the owner of the Hotel, with the purpose of reducing the number and the complexity of business relationships established with other firms. See Candela G. and P. Figini (2003).

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Araman V., J. Kleinknecht and R. Akella (2003). Coordination and RiskSharing in E-Business, Working Paper, Stanford University, Stanford, CA. Ayres I. and R. Gertner (1992). Strategic Contractual Inefficiency and the Optimal Choice of Legal Rules, 101 Yale Law Journal, 729–73. Arrow K.J. (1971). Essays in the Theory of Risk Bearing, Markham, Chicago. Barnes-Schuster D., Y. Bassok and R. Anupindi (2000). Coordination and Flexibility in Supply Contracts with Options, Working Paper, University of Chicago. Candela G. and P. Figini (2003). Economia del turismo, McGraw-Hill, Milano. Candela G. and P. Figini (2005). Economia dei sistemi turistici, McGraw-Hill, Milano. Candela G. and R. Cellini (2004). Investment in Tourism Market: A Dynamic Model of Differentiated Oligopoly, Working Paper, Social Science Research Network (SSRN) Electronic Paper Collection, No. 20.2004, Fondazione Enrico Mattei Research Paper Series, Milano. Cheung Steven N.S. (1969). Transaction Costs, Risk Aversion, and the Choice of Contractual Arrangements, Journal of Law and Economics 12, 23–46. Cooper C.P., J. Fletcher, D. Gilbert, R. Shephard and S. Wanhill (1998). Tourism: Principles and Practice, Addison Wesley Longman Publishing, New York. Cooter R. and T. Ulen (2004). Law and Economics, Pearson Addison Wesley. Faure M. and S. Goran (2003). The Economic Analysis of Environmental Policy and Law, Edward Elgar Publishing Limited, Cheltenham, UK. Diamond P. and M. Rothschild (1978). Uncertainty in Economics: Readings and Exercises, Academic Press, New York. Fisher F.M. (1983). Disequilibrium Foundations of Equilibrium Economics, New York, Cambridge University Press. Hahn F.H. and T. Negishi (1962). A Theorem on Non-Tatˆ onement Stability, Econometrica, 30, 463–69. Hart O.D. and B. Holmstrom (1987). The Theory of Contracts, Advances in Economic Theory, T.R. Bewley (eds.), Fifth World Congress, Cambridge University Press, Cambridge, 369–98. Hirshleifer J. and J.G. Riley (1992). The Analytics of Uncertainty and Information, Cambridge University Press, Cambridge, UK. Holmstrom B. and J. Tirole (1989). The Theory of the Firm, Handbook of Industrial Economics, Schmalensee R. and R.D. Willig (eds.), New York, Elsevier Science Publishing, 61–133. Li C. and P. Kouvelis (1999). Flexible and Risk-Sharing Supply Contracts Under Price Uncertainty, Management Science, 45, 10, 1378–1398. Macaulay S. (1963). An Empirical View of Contract, 1985 Wisconsin Law Review, 465–482. Machina M. (1987). Choice under uncertainty: Problems solved and unsolved, The Journal of Perspectives, 1: 121–54. Mas-Colell A., M.D. Whinston and J.R. Green (1995). Microeconomic Theory, Oxford University Press, New York, Oxford.

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Masten S.E. (1988). Equity, Opportunism, and the Design of Contractual Relations, Journal of Institutional and Theoretical Economics, 144, 180–195. Masten S.E. (1999). Contractual Choice, Encyclopaedia of Law & Economics, B. Boukaert and G. De Geest (eds.), Edward Elgar Publishing, Cheltenham, U.K. McKenna C.J. (1986). The Economics of Uncertainty, Wheatsheaf Books, Brighton. Moutinho L. (1987). Consumer Behavior in Tourism, European Journal of Marketing, 21 (10): 5–44. Quandt R. (1988). The Econometrics of Disequilibrium, Basil Blackwell Ltd, Oxford, UK. Rothschild M. and J. Stiglitz (1970). Increasing risk I: A definition, Journal of Economic Theory, 2: 225–43. Shavell S. (1984). The Design of Contracts and Remedies for Breach, Quarterly Journal of Economics, 98, 121–148. Shelanski H. and P.G. Klein (1995). Empirical Research in Transaction Cost Economics: A Review and Assessment, Journal of Law, Economics, and Organization, 11, 335–61. Schwartz A. (1992). Legal Contract Theories and Incomplete Contracts, Contract Economics, Werin L. and H. Wijkander (eds.), Cambridge, MA, Basil Blackwell, 79–108. Stiglitz J. (1974). Incentives and Risk-Sharing in Sharecropping, Review of Economic Studies, 41, 219–255. Telser L.G. (1980). A Theory of Self-Enforcing Agreements, 53 Journal of Business, 27–44. Tirole J. (1988). The Theory of Industrial Organization, Massachusetts Institute of Technology, Boston, Mass. Tirole J. (1994). Incomplete Contracts: Where Do We Stand?, Walras-Bowley lecture, delivered at the North American Summer Meetings of the Econometric Society, Quebec City. Tisdell C. (2000). The Economics of Tourism, Elgar Reference Collection, International Library of Critical Writings in Economics, Cheltenham, UK. Tsay A. (1999). The Quantity Flexibility Contract and Supplier-Customer Incentives, Management Science, 45, 10, 1339–1358. Werin L. and H. Wijkander (1992). Contract Economics, Cambridge, MA, Basil Blackwell Ltd. Williamson O.E. (1979). Transaction-Cost Economics: The Governance of Contractual Relations, Journal of Law and Economics, 22, 233–262.

Is Tourism Specialization Sustainable for a Small Island Economy? A Cyclical Perspective Sauveur Giannoni1 and Marie-Antoinette Maupertuis2 1 2

IDIM, University of Corsica, BP 52, 20250 Cort´e, France [email protected] IDIM, University of Corsica, BP 52, 20250 Cort´e, France [email protected]

1 Introduction While tourism is becoming one of the most important sectors of the world economy, the number of small islands trying to develop a competitive tourism sector is increasing (Logossah, 2004; Shareef, 2003). Observers have to recognize that an important part of these Small Island Tourism Economies (SITE) is successful. Lanza and Pigliaru (2000) showed that seven out of the fifteen fastest growing countries between 1985 and 1995 were tourism countries3 and that most of them were SITE as St Kitts and Nevis, Antigua and Barbuda or Cyprus. Marques (2003) showed for example a strong correlation between the level of GDP and tourism receipts for the region of the Caribbean. SITE are said to have some kind of absolute advantage in the production of tourist goods because of their natural endowments, and it implies that tourism is often seen by policy makers as the easiest way out to the “small island problem” (Crusol et al. 1989). Nonetheless tourism must not be thought of as a panacea, as a study realized by the World Bank and the Commonwealth secretariat pointed out in 2000. In fact, even though tourism provides welfare gains (Copeland, 1991, Nowak, Sahli, 1999, Sinclair 1998) and constitutes a good engine of growth (Hazari and Sgro, 1995, Lanza et al. 2003), it may also generate static and dynamic perverse effects. Recent literature has widely explored negative economic consequences of tourism 3

More than 30% of GDP stems from tourism.

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specialization for small open economies: dependence on foreign capital, inflation, depandence to volatile demand (Sinclair, 1998), labor market disturbances (Nowak, Sahli, Sgro, 2003), Dutch disease effect (Nowak, Sahli, 1999), land competition, low-education trap (AugereauVeron, Augier, 2003). All these papers emphasize the risk for small open economies specialized in tourism to become rent-based economies (such as petroleum ones) and all associated drawbacks. Tourism specialization seems to be a fruitful strategy in the short run but a competences destroying one in the long term. The reviewed literature does not focus on what is at stake in our opinion: the deplation of the natural capital. The major part of tourism income relies on the direct or indirect exploitation of SITE’s environmental assets. It is well known that island ecosystems are greatly sensitive to human pressure.4 Sustainability of tourism means in a very naive acception to ensure the preservation of the environment while developing tourism activities. But tourism development may only be achieved if the local economy supplies all the services that visitors deserve in order to enjoy a good experience. Furthermore, the quality of the experience also depends on the availability of natural assets. The problem is that the construction of infrastructures generates damages to the ecosystem. The aim of this paper is to explore the inter-temporal tradeoff between tourism intensive investments and environmental quality preservation needed to ensure population income in the long run. In this perspective, we propose a simple dynamic model of tourism development. Recent modeling was produced on this subject using a prey-predator framework (Casagrandi, Rinaldi, 1999, 2002; Kort et al. 2002). Both models develop an optimal control framework highlighting two kinds of trajectories. The first one is a situation in which the number of tourists remains constant over time. A second possible path is given by a cyclical evolution in investments, in the number of tourists, in the pollution stock and in infrastructures inducing different phases in the tourism development and confirming Butler’s (1980) lifecycle hypothesis. Tourism development may be sustainable, from the environmental viewpoint, because 4

Islands usually have typical and fragile ecosystems (lagoons, mountains, rivers, etc.) due to specific and unique climatic, geographical and geological characteristics.

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of alternative phases of high and low frequentation that enables the ecosystem to regenerate. The paper is organized as follows. The first section reviews the environmental drawbacks arising from tourism specialization in a small island and recalls what the Butler’s lifecycle hypothesis was. The second presents the model developed by Kort et al. (2002) and Casagrandi and Rinaldi (2002). From the discussion of their results, we can conjecture that an appropriate investment strategy along the lifecycle would prevent the island destination from experiencing a declining phase thanks to environmental regeneration. The third section describes a model of tourism investments introducing the perceived environmental quality as a state variable. Our model confirms Butler’s life cycle hypothesis and shows that even in the absence of external effect, the destination experiences a cycle. We highlight the role of investment decisions on the decline of the number of tourists. When the perceived environmental quality is decreasing, tourism lobby is incited to disinvest in order to preserve the environment and it explains the cycle. The last section concludes and opens up further research on differentiation strategies that small islands may implement in order to achieve sustainable development.

2 Environmental Drawbacks and Sustainability of Tourism Developing a competitive tourism sector implies for a small island the exploitation of its natural rent. Tourism specialization is driven by the high environmental quality of the island as it constitutes an important attraction factor on the world market of tourism. So, tourists will come and visit a small island in order to enjoy its extraordynary landscapes or the great quality of its coastal waters. We have to agree with Punzo and Bimonte (2003) when they define the tourism good as an experience one. The tourist comes in order to experience an unique trip and may come back if his expectations were fulfilled. Yet, tourism development may be at the basis of the environmental degradation. In order to welcome tourists, an island needs to build lodging infrastructures and the multiplication of such infrastructures generates visual pollution leading to a degradation of the experience.

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Furthermore, when infrastructures are abandoned they can be seen as an irreversible damage for the destination. Then the tourist flow in the island is synonim of an increase in the production of wastes and when it goes over a given threshold the environment is unable to assimilate it.5 So, the tourist development is going to fragilise the insular ecosystem already known to be especially sensitive. Developing the lodging capacities and the tourist flow in a few sites will generate an increased environmental pressure. It may lead to irreversible damages such as the destructions of natural habitats and finally the disparition of species from the insular ecosystem. So, tourism development of the island has more or less irreversible harmful effects. All the stakeholders, from the private sector or representatives of the local community, are aware of these perverse effects. For the host community, a first possible behaviour is to ignore these effects because people are only interested with short run profits. This may reflect a lack of information which isusually associated with poverty of the local community or simply with the greedy behaviour of firms going away as soon as the natural resource is exhausted. The second behaviour is much more plausible. All the stakeholders are looking for a sustainable trade-off between tourism exploitation and environmental conservation which is a way to conciliate economic profitability and attractiveness of the island for the tourists in the long run. Following to the World Tourism Organisation (1995), tourist development is sustainable if it enhances the way of living of the population without damaging the environmental and cultural capital of the destination. But there is no real criterion in order to measure the sustainability of a given development strategy. As said by Hawks and Williams (1993), sustainability is a challenge for the island and it has to increase the lodging capacity and the quality of services without having perverse effects on the environment. In the island case, it seems that tourism must be seen as sustainable if it is possible in the long run to satisfy an increasing demand, major source of income, without damaging the natural attractions of the island on the tourism market: coastal areas, animal species, unique landscapes, resources in drinkable water. Then all the problem is to find an equilibrium point between resource exploitation and resource preservation because it is the only way 5

Think, e.g. of the many plastic bags floating on the water.

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to maintain attractiveness over time. This equilibrium has to be found in the evolution of the tourist flow. By observing the evolution of tourist flows in a few destinations Butler (1980) identifies what he calls “a lifecycle of destinations” due to a cyclical evolution of the frequentation. A first plausible cause of the cyclical frequentation is the “crowding effect”. As we already said, tourists are looking for the best possible experience but it will be worsen by the arrival of an increasing tourist flow. So from that point, we will have a fall in the number of tourists because the tourist flow is “destroying” the destination (see Fig. 1). A second cause we think of is the declining environmental quality of the destination. Recent literature explains the different phases in the lifecycle by the interaction between infrastructures investments and pollution.

Rejuvenation A B Stagnation

CRITICAL RANGE OF ELEMENTS OF CAPACITY

Consolidation

C

NUMBER OF TOURISTS.

Decline D

E

Development

Involvement

Exploration TIME

Fig. 1. The lifecycle of destinations Source: Butler (1980)

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3 Tourism Sustainability: A Lobby Profit Perspective Theoretical models of tourism sustainability have been recently produced (Casagrandi, Rinaldi, 1999, 2002; Greiner et al. 2001; Kort et al. 2002; Candela and Cellini, 2004) following the prey-predator general background. They focus on the intertemporal trade-off between tourism investments (I) in infrastructures and preservation of natural capital that are both at the basis of the tourism attractiveness of the destination. The purpose is to determine the optimal development of tourism (i.e. tourism infrastructures) taking into account that i) more infrastructures attract more tourists but increase directly and indirectly the stock of pollution ii) tourists are both pollution and frequentation adverse. We test here the capability of this kind of models to be applied to our island perspective. The local tourism lobby is assumed to maximise the intertemporal profit extracted from tourism specialization knowing that each tourist pays its journey at a given price p. It is assumed that perfect competition prevails on international tourism market and then that the destination cannot influence the price of a journey. Tourist lobby has to support three kinds of costs: – costs relative to infrastructures investments increasing with I (c(I)), – maintenance infrastrucures costs and associated services costs (k1 et k2 ). Kort et al. (2002) suppose that the number of tourists (T) arriving in the island depends positively on the number and the quality of infrastructures. Nonetheless, a crowding effect (bT) exists, that affects negatively the number of tourists. Infrastructures and tourists generate pollution (P) which affects negatively the attractiveness of the place. Then the tourist lobby decision program may be written as follows: 

∞

e

Π = max I

−rt

0

 πt dt = 0

∞

e−rt (pT − c(I) − (k1 + k2 )S) dt

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subject to6 ˙ S(t) = I(t) − δS(t), S(0) = S0 > 0 ˙ T (t) = a (S(t), k1 , P (t)) − bT (t), T (0) = T0 > 0 P˙ (t) = σS(t) + τ T (t) − α (P (t)) , P (0) = P0 > 0. The first constraint reflects the evolution of the infrastructures stock, it means the net investment. The second relation describes the evolution of tourist frequentation and is simply the difference between tourism attractiveness, a (S(t), k1 , P (t)), and the crowding effect, bT. The last differential equation sketching the evolution of the pollution stock needs some comments. σ and τ respectively represent the contribution of an additional unit of infrastructure and an additional tourist to the stock of pollution. α(P) is the absorption capacity of the environment.7 Kort et al. (2002) apply the Pontryagin’s maximum principle to derive insights on the structure of the optimal trajectory by introducing the current-value Hamiltonian: H(.) = pT − c(I) − (k1 + k2 ) S + λ1 (I − δS) + λ2 [a (S, k1 , P ) − bT ] + λ3 [σS + τ T − α (P )] . The necessary conditions associated to the Hamiltonian are given by: c (I) = λ1 ,

(1)

At time t the marginal of infrastructures is equal to the shadow price of infrastructures.8

6 7 8

λ˙ 1 = (r + δ) λ1 + k1 + k2 − λ2 aS (.) − λ3 σ

(2)

λ˙ 2 = (r + b) λ2 − p − λ3 τ

(3)

  λ˙ 3 = r + α (.) λ3 − λ2 aP (.)

(4)

aSS < 0, aSP < 0, aPP < 0 for low values of P, aPP > 0 for high values of P. It is noteworthy that the choice of the functional form of this capacity (linear versus inverted U-shaped) will determine the result obtained. From the implicit functions theorem, we know that the higher the implicit value of an infrastructure will be the more investment there will be.

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Inter-temporal optimality conditions. aj denotes the derivative of a with respect to the variable j. Following the authors, we assume a steady-state solution that will authorize a qualitative study of the dynamic of the system using simulations. The conditions for a steady state to exist are: I (λ∗1 ) = δS ∗ ∗

(5)

∗

a (S , k1 , P ) = bT ∗

∗

∗

α (P ) = σS + τ T

(6)

∗

(r + δ) λ∗1 = λ∗2 aS (.) + λ∗3 σ (r + b) λ∗2 = p + λ∗3 τ   r + α (.) λ∗3 = λ∗2 aP (.)

(7) − k1 − k2

(8) (9) (10)

Equation (5) shows that the optimal investment is a replacement one. Equation (6) recalls that, in equilibrium, attractiveness must exactly offset the crowding effect. Equation (10) shows that the pollution due to infrastructures and tourists arrivals must be totally absorbed by the environment. Numerical simulations implemented by Kort et al. (2002) show that according to critical values of the discount rate r the described system follows two different evolutions: – for r > rcritical , the system converges toward a saddle path equilibrium.9 – for r < rcritical , the system steadily oscillates around the steadystate.10 These results corroborate the analysis of Casagrandi and Rinaldi (2002) who identified both trajectories as two special issues of their broader dynamic analysis. Let us explain the first scenario: the saddle-path means that in the long run the island would converge toward a stationary equilibrium where investments are those just necessary to offset infrastructures depreciation, where the number of tourists is constant over time and where each produced unit of pollution is absorbed by the environment. The conclusion is that the number of tourists is constant in the long run. 9 10

For mathematical conditions see Kort et al. (2002). Briefly speaking, we can say that according to the value of the discount rate a Hopf bifurcation may appear that is a stable limit cycle.

Is Tourism Specialization Sustainable for a Small Island Economy?

S

.20–

.280

.19

.275

.18

.270

.17 .16

I

P

T

5.00

.025

.265

.020

.260 4.90

.255

.015

.250 .14

4.85

.245

.12

.030

4.95

.15

.13

95

.010

.240 4.80

.235

.005

.11 –2

0

t1t2

2

4

t3

6

8

t4

10

t5t6

12

14 16

t7

18

20

22

24

t8

Fig. 2. Evolution of the variables over-time for low values of the discount rate

Source: Kort et al. (2002)

In the second scenario: for low values of the discount rate – that is for weak values of time preferences – a limit cycle appears and the tourism industry profit as well as investments experiment expansion and recession phases as hypothesized by Butler (1980). This scenario is depicted on Fig. 2. We have yet interpreted this evolution from our island perspective as follows (Giannoni, Maupertuis, 2004). If we consider a small beautiful island, with a very low tourism infrastructures capacity, which decides to implement a tourism specialization strategy, it will experience according to the previous model various stages of development: – The prosperity phase With initial high endowments in environmental capital and low level of infrastructures, the number of tourists is weak. Additional infrastructures and a clean environment attract more and more tourists. Investment is increasing in order to gain rapidly more and more profit but then infrastructures generate indirect perverse effects. A weak investment is then optimal as the number of tourists keeps on growing. – The saturation phase The number of tourists reaches a peak as pollution is increasing. A reduction of the tourist flow is needed in order to lower pollution.

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– The declining phase There are few tourists in the island and the environment regenerates. The optimal behaviour is to increase again investment in order to welcome tourists again in the future. Cash flow is weak as tourists are few and investments are costly. – The recovering phase The number of tourists increases as the island is endowed in infrastructures and environment is clean. We can conjecture from our island perspective that an appropriate investment strategy along the lifecycle would prevent the destination from a declining phase if environment regenerates. Nonetheless, the story described by Kort et al. (2002) is written using very specifical formalizations as a pollution stock depending from an absolute absoption capacity of the ecosystem. Moreover, this model – despite it authorizes to simulate a cyclical scenario – remains unsolved and small variations in parameters give radically different evolutions. Nonetheless, we try to ask for the validity of such a cyclical scenario simplifying the model in order to find a solution. In this perspective we introduce the perceived environmental quality as a state variable instead of a pollution stock.

4 Simple Model of Optimal Investment in Tourism with Environmental Quality In this section, our aim is to build a simpler optimal investment model than the one built by Kort et al. (2002). It seems to us more suitable to take into account the environmental quality as it is perceived by tourists visiting the island. On that point, we follow recent literature on sustainable tourism (Lanza, 1995; Hernandez, L´eon, 2003). 4.1 The Model We consider, as in Kort et al. (2002), the point of view of a tourist lobby the problem of whom is to maximise the actualised sum of profits that the island will receive over time in selling tourist journeys. ∞

−ρt

e 0 11

∞ (π) =

e−ρt (pt Tt (St , Qt , pt ) − c(It ) − d(St ))dt11

0

From now on, we will skip the temporal index t.

(1’)

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with: T the number of tourists, S: the number of infrastructures, Q: the perceived quality of the environment c(I): investment cost at time t and d(S): maintenance costs at time t. We suppose that the price of a journey is exogenously given and constant over time what is consistent with the standard hypotheses of smallness of the island and of perfect competition between destinations. The profit at time t is increasing with the number of tourists Tt . Profit is decreasing with the cost of investment and with the cost of maintaining infrastructures. The number of tourists is monotonically increasing with the perceived environmental quality. Reciprocally, if the environment deteriorates too much, the tourists will not be incited to come because of the bad environmental quality. Further more, the number of infrastructures is increasing with the amount of investment but lower because of their depreciation. And, environmental quality is strictly decreasing with the number of tourists. In order to solve our model we will use simple functional forms given below. The number of tourists at time t is given by: T (S, Q, p) = (αS + βQ) p−ε where α, β > 0 and ε ≥ 0.

(2’)

This way to define tourism demand is quite simple and may appear unrealistic. But we assume that demand is iso-elastic in order to make the model more tractable. 1 2 c1 I with c1 > 0 (3’) 2 Maintenance costs are given by: d(S) = c2S with c2 > 0 (4’) The flow of infrastructures at t is given by: S˙ = I − δS with δ > 0 (5’) Investment cost is defined as:

c(I) =

The variation of environmental quality is given by:   Q˙ = rQ − σS − τ T = rQ − σS − τ (αS + βQ)p−ε .12

(6’)

This formulation needs to be explained. r is a positive parameter reflecting the capacity of the ecosystem to regenerate. To be clear, in 12

σS denotes the negative effect of an infrastructure on the environment because it exists and τ αS the indirect negative environmental effect of an infrastructure because it attracts more tourists.

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the absence of human activity (with T = 0 and S = 0), environmental quality is monotonically increasing at the rate r. As an example, we can think to the evolution of an endemic plant variety. In the absence of tourists, the number of plants will grow at a given rate r, increasing biodiversity, but the presence of tourists is going to limit this growth13 and finally in the case where rQ < σS + τ (αS + βQ), the quality of the environment is going to decrease. Finally, the tourism lobby has to solve the following program: ∞ Max

  1 e−ρt (p (αS + βQ)p−ε − c1 I 2 −c2 S)dt 2

(7’)

0

s.t. S˙ = I − δS

  Q˙ = rQ − σS − τ (αS + βQ)p−ε S(0) = S0 ≥ 0, Q(0) = Q0 ≥ 0.

(8’) (9’)

The Hamiltonian of the program is written as follows:  1  H = p (αS + βQ)p−ε − c1 I 2 − c2 S + λ1 (I − δS) 2    + λ2 rQ − σS − τ (αS + βQ)p−ε .

(10’)

The first order conditions are: ∂H = 0 ⇔ λ1 = c1 I (11’) ∂I ∂H = λ1 ρ − λ˙ 1 ⇔ λ˙ 1 = λ1 (δ + ρ) + λ2 (σ + τ αp−ε ) + c2 − p(1−ε) α ∂S (12’)   ∂H = λ2 ρ − λ˙ 2 ⇔ λ˙ 2 = λ2 τ βp−ε + ρ − r − p(1−ε) β. (13’) ∂Q From (8’), (9’), (11’), (12’), (13’), we obtain a dynamic system of four linear equations with four unknowns that we solve.14 We find that the system is converging toward a steady-state value and that this steady-state is a saddle-point provided that the capacity of the ecosystem to regenerate is low enough (r < τ βp−ε ). 13 14

Stamping, gathering, ripping off usually practiced by tourists. See the appendix for the full solution of the model and a numerical simulation of the model.

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Steady-state values of the variables are given by:  1−ε  p α − c2 (τ βp−ε + ρ − r) − p1−ε β (σ + τ αp−ε ) ∗ S = (τ βp−ε + ρ − r) (ρ + δ) c1 δ    (σ + τ αp−ε ) p1−ε α − c2 (τ βp−ε + ρ − r) − p1−ε β (σ + τ αp−ε ) ∗ Q = (r − τ βp−ε ) (τ βp−ε + ρ − r) (ρ + δ) c1 δ  1−ε  p α − c2 (τ βp−ε + ρ − r) − p1−ε β (σ + τ αp−ε ) I∗ = (τ βp−ε + ρ − r) (ρ + δ) c1 p1−ε β λ∗2 = τ βp−ε + ρ − r ∗ (14’) T = p−ε [αS ∗ + βQ∗ ] . Parameters values are such that the steady-state is economically consistent. It means that S∗ > 0 and I∗ > 0. We observe that: – In the long run the perceived environmental quality Q∗ is negative. It means that in the long run profit may be negative because of the bad environmental quality depending on tourist preferences. – λ∗2 is the value of a marginal increase in the environmental quality. We can see that λ∗2 > 0 so that in the long run the preservation of an additional unit of environmental quality increases the profit. 4.2 Economic Interpretation of the Results We have just shown that our model is asymptotically converging toward a saddle-point stable steady-state value. This implies that we have found in a quite simpler model – and considering environmental quality instead of pollution level – the same qualitative result than Kort et al. (2002). But we cannot identify any limit-cycle because the appearance of such a trajectory is the result in Kort et al. (2002) of non-linear formalisation. Nonetheless, it is possible to give an economic interpretation of the result in order to understand the mechanism leading, especially in the insular case, to the observance of a cycle. First, consider an island initially endowed with little infrastructures and a high environmental quality, it may potentially attract lots of tourists but investment is needed in order to develop the lodging capacity.

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In that case, the lobby will be incited to invest and the profit will be low (may be negative) but increasing as the number of tourists is also increasing. Anyway, as the investment cost is growing, the number of infrastructures is increasing at a decreasing rate. Further more, as the number of tourists is increasing the pressure on the ecosystem is also increasing . Because of the increasing costs and of the increasing environmental pressure, in the long run the lobby will be encouraged to disinvest15 and then in the long run, only invest to replace obsolete infrastructures. If the lobby goes on investing in new infrastructures, the result will be a decrease in the environmental quality leading to a dramatic fall in frequentation. But in the meantime the cost of investing and maintaining infrastructures are both increasing. So, in that conditions it is not a rational behaviour to go on investing. We understand that when the lobby invest, there is an increase in frequentation but as a result of the environmental pressure, we then have a rapid fall in the number of tourists toward the steady-state frequentation, that is a plateau. In fact, the optimal investment policy of the lobby leads to the observance of a cycle similar to the one described by Butler. The existence of such a mechanism means that the number of tourists that an island would attract in the long run is constant. Yet, our analysis also explains what is happening when environmental quality is degraded. To understand this result, we have to consider that the island has reached a situation characterized by a high number of infrastructures and a poor environmental quality. In such a situation, the number of tourists is declining as the poor environmental quality is taking them away from the island. Then, the lobby is incited to let the number of infrastructures decline in order to save the maintenance costs. So, the island will once more be naturally driven toward the steady-state. So these two results illustrate the scenario that Butler considered as one of the most plausible, a fall in the number of tourists until it reaches a plateau.16 As we are concerned about the profits associated with tourism, we ask for the effect of a change in the price of a journey on the previous results. How change in price affects ceteris paribus the steady-state 15 16

Disinvest means that the net investment is negative. See trajectory C in Fig. 1 and Figs. 3 and 4.

Is Tourism Specialization Sustainable for a Small Island Economy? Little Infrastructures High Environmental Quality Few Tourists

Strong Incentive to Invest

Replacement Investment Constant Frequentation Constant Environmental Quality

Decreasing Incentive to Invest Decrease in Frequentation

101

Increasing Number of Infrastructures Growing Frequentation

Increasing Environmental Pressure Increasing Costs

Fig. 3. The developing island case

values of the variables? The effect of an increase in price ceteris paribus is compliant with the standard results of environmental economics. An increase in price leads to a fall in frequentation and as a consequence to an increase in environmental quality in the steady-state. If the decline in the number of tourists is not harmful for the profit, a good policy would be to increase the price of a journey. Of course, a necessary condition is that the island has some kind of market power but we do not explore such a scenario in this paper.

Lots of Infrastructures Low Environmental Quality High but Decreasing Frequentation

Replacement Investment Constant Frequentation Constant Environmental Quality

Strong Incentive to Disinvest (to avoid maintenance costs)

Decreasing Number of Infrastructures Dramatic Fall in Frequentation

Stabilisation of the Incentive to Invest

Fig. 4. The over-developed island case

Decreasing Environmental Pressure Decreasing Costs Little Increase in Frequentation

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5 Conclusions The aim of this paper was to explore the inter-temporal trade-off between tourism intensive investment and environmental quality preservation needed to ensure local tourism lobby income in the long run. In this perspective, we proposed a simple dynamic model of tourism investment introducing the environmental quality as a state variable of the dynamic system. Despite limits inherent to the simplicity of the framework, our result confirms the Butler’s lifecycle hypothesis. Our specific contribution is to show that even when the agents are aware of the environmental drawbacks of tourism development, the optimal investment policy leads to the observance of a cycle. To put it differently, even in the absence of external effect, tourism slows down in the long run after a phase of boom. According to us, the next step of our research is to study the opportunity for an island to differentiate its product. A monopolistic competition framework with several destinations competiting in the tourism industry could be of interest.

References Augereau-Veron E., Augier L. (2003) “Tourisme, Education et Croissance ´economique”, contribution to the conference Tourisme et D´eveloppement Durable, CEREGMIA, Universit´e des Antilles et de la Guyane, 25–27 September. Bimonte S., Punzo L. (2003): “Turismo e Sviluppo Locale nei Sistemi Microinsulari”, EdADTS Working Paper Series n◦ 1, July. Butler R.W. (1980), “The Concept of a Tourist Area Cycle of Evolution: Implications for management of resources”, Canadian Geographer, XXIV, 1. Candela G., Cellini R. (2004), “Investment in tourism market: a dynamic model of differentiated oligopoly”, FEEM Working paper series number 20.2004. Casagrandi R., Rinaldi S. (1999), “A theoretical approach to tourism sustainability”, Politecnico di Milano Working paper. Casagrandi R., Rinaldi S. (2002), “A theoretical approach to tourism sustainability”, Conservation Ecology, vol. 6 issue 1. Commonwealth secretariat and World Bank (2000), Small States: Meeting Challenges in the global economy, Final Report of the task force, April. Copeland B.R. (1991), “Tourism, welfare and de-industrialisation in a small open economy”, Economica, 58, (November), 515–529. Crusol J, Hein Ph., Vellas F. (1989), L’enjeu des petites ´economies insulaires, Economica.

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Giannoni S., Maupertuis M.-A. (2004), La sp´ecialisation touristique estelle soutenable pour une petite ´economie insulaire ?, Third International Congress Environment and Identity in the Mediterranean, University of Corsica, Corte, (July) Hawkes S. et Williams P. (1993), “The Greening of Tourism: From Principles to Practice”, GLOBE ’92 Tourism Stream: Case Book of Best Practice in Sustainable Tourism, Simon Fraser University, Burnaby, B.C. Hazari B., Sgro P.M. (1995) “Tourism and growth in a dynamic model of trade”, The Journal of Trade and Economic Development, 4(2). Hazari, Nowak, Sahli (2003), “Tourism, trade and domestic welfare”, Contribution to the CRENoS conference Tourism And Sustainable Economic Development, Chia, Sardinia, September the 19–20th. Kort P.M, Greiner A., Feichtinger G., Haunschmied J.L., Novak A., Hartl R.F. (2002), “Environmental effects of tourism industry investment: an inter-temporal trade-off”, Optimal Control Applications and Methods, 23, 1–19. Lanza , Pigliaru (1995), “The Tourism Sector in the Open Economy”, in Nijkamp P., Coccossis W. (Eds), Tourism and the Environment, Avebury: Aldershot. Lanza A., Pigliaru F. (2000), “Why are tourism countries small and fastgrowing?”, CRENoS Working Paper. Lanza A., Temple P., Urga G. (2003), “The implications of tourism specialization in the long run: an econometric analysis for 13 OECD economies”, Tourism Management, 24, pp. 315–321. Leon C., Hern´ andez J.M., Gonz´ alez M. (2003), “Endogenous life cycle and optimal growth in tourism”, contribution to CRENoS conference Tourism And Sustainable Economic Development, Chia, Sardinia, September the 19–20th. Logossah K., Fortuna M., Maupertuis M-A, Salmon J-M, (2004), “SIDS and the tourism industry”, chapter 10 of Small island developing States in the global economy: selected issues, UNCTAD publication, edited by P. Encontre and Ph. Hein, Gen`eve, June. Marques B. (2003), “Tourisme et croissance dans la Cara¨ıbe: l’exp´erience des vingt derni`eres ann´ees”, Master thesis, CEREGMIA, Universit´e des Antilles et de la Guyane Nijkamp P., Coccossis W. (Eds) (1995), Tourism and the Environment, Avebury: Aldershot. Nowak J.J, Sahli M. (1999), “L’analyse d’un boom touristique dans une petite ´economie ouverte”, Revue d’Economie Politique, 109, 5, pp. 729–749. Sinclair M.T (1998), “Tourism and Economic Development: A Survey”, The Journal Of Development Studies, vol. 34, n◦ 5, June. World Tourism Organisation (1995), “Lanzarote Charter for Sustainable Tourism”

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Appendix A1.

Solution of the model ⎤ ⎡ ⎡ ˙ ⎤ S 0 ⎥ ⎢ ⎢ Q˙ ⎥ 0 ⎥ ⎢ ⎥=J +⎢ ⎣ c2 − p1−ε α ⎦ ⎣ λ˙ 1 ⎦ −p1−ε β λ˙ 2

⎤ 1 0 −δ 0 c1 ⎥ ⎢ − (σ + τ αp−ε ) r − τ βp−ε 0 0 ⎥ ⎢ with: J = ⎢ 0 0 ρ + δ σ + τ αp−ε ⎥ ⎥ ⎢ ⎣ 0 0 0 τ βp−ε + ρ − r ⎦ ⎡

⎤ −δ ⎢ r − τ βp−ε ⎥ ⎥ Eigenvalues of J are: EV = ⎢ ⎦ ⎣ ρ+δ −ε τ βp + ρ − r ⎡

Assuming that r < τ βp−ε we see that the sign of the eigenvalues alternates. It is enough to say that the optimal path is a saddle. From that point, the solution of the system is: ⎤ ⎡ pε r+pε δ−βτ ⎤ ⎡ ⎤ ⎡ 0 St pε σ+ατ ⎥ ⎢ ⎢ ⎥ ⎢ Qt ⎥ 1 −δt ⎥ + e(r−τ βp−ε )t A2 ⎢ 1 ⎥ ⎥ ⎢ ⎢ ⎦ ⎣ ⎣ λ1t ⎦ = e A1 ⎣ 0⎦ 0 λ2t 0 0 ⎤ ⎡ 1−ε −ε 1−ε −ε

(p

α−c2 )(τ βp

+ρ−r)−p

β (σ+τ αp

)

(τ βp−ε +ρ−r)(ρ+δ)c1 δ ⎥ ⎢ ⎥ ⎢ −ε 1−ε ⎢ (σ+τ αp )[(p α−c2 )(τ βp−ε +ρ−r)−p1−ε β (σ+τ αp−ε )] ⎥ ⎥ ⎢ (r−τ βp−ε )(τ βp−ε +ρ−r)(ρ+δ)c1 δ ⎥ ⎢ +⎢ ⎥ ⎢ (p1−ε α−c2 )(τ βp−ε+ρ−r)−p1−ε β (σ+τ αp−ε) ⎥ ⎥ ⎢ (τ βp−ε +ρ−r)(ρ+δ) ⎥ ⎢ ⎦ ⎣ 1−ε β p τ βp−ε +ρ−r

where:



  1−ε  p α − c2 (τ βp−ε + ρ − r) − p1−ε β (σ + τ αp−ε ) A1 = S0 − (τ βp−ε + ρ − r) (ρ + δ) c1 δ pε σ + ατ pε r + pε δ − βτ

Is Tourism Specialization Sustainable for a Small Island Economy?

and



A2 = Q0 −

A2.

σ + τ αp−ε

105

    p1−ε α − c2 τ βp−ε + ρ − r − p1−ε β σ + τ αp−ε − A1 (r − τ βp−ε ) (τ βp−ε + ρ − r) (ρ + δ) c1 δ

 

Simulation

In this section of the appendix, we present a simulation of the model for a given set of parameters. The simulation illustrates that the island behaves as predicted by Butler’s lifecycle (see Figs. 5 and 6.) Parameters

α β p ε σ τ R 50 10 1 1.05 0.05 0.1 10−5

c1 c2 δ ρ 0.1 0.1 0.1 0.03

Number of tourists 34000 33800 33600 33400 33200 50

100

150

200

Time

Fig. 5. Simulation results of life cycle model for tourists

Profit 31800 31600 31400 31200 31000 50

100

150

200

Time

Fig. 6. Simulation results of life cycle model for profit

Efficiency in a Chain of Small Hotels with a Stochastic Production Frontier Model ´ Carlos Pestana Barros1 and Alvaro Matias2∗ 1

2

Instituto Superior de Economia e Gestao, Technical University of Lisbon, Rua Miguel Lupi, 20, 1249-068 Lisbon, Portugal [email protected] ´ University of Evora, Urban and Regional Analysis and Research Laboratory, ´ Largo dos Colegiais 2, Apartado 94, 7002-554 Evora, Portugal [email protected]

1 Introduction Within the accommodation industry, small hotels have their own specificity that should be taken into account when analyzing their efficiency. Presumably, small hotels preserve traditions while offering a personal service in a welcoming atmosphere. Therefore, it seems fair to assume that the client is more likely to feel like a guest, in the true sense of the word. However, small hotels face an increasingly intense competition generated by medium and large size national and multinational hotel chains. These chains offer standardized architecture, rooms and services, which often result in a sterile, impersonal environment that is not conducive to providing a unique or unforgettable experience. Nonetheless, the key to such chains’ success is in concealing their standardization by catering for clients’ needs through efficiency and service quality. The large chain hotels, in particular, are more likely to be in highly accessible, central locations and aim to attract key market targets (i.e. business travelers and package tourists) by means of discounts, loyalty inducements and rates negotiated with carriers and tour operators. In contrast, small hotels suffer from their lack of economies of scale and scope which, in turn, affect turnover and profitability. Furthermore, their peripheral location, reliance on passing trade and local advertising, as well as an unclear conception of their clientele and a perceived ∗

The authors are grateful to two reviewers for their comments and valuable contributions to clarify and improve the paper. The usual disclaimer applies.

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lack of competitive advantage keeps them isolated from the industry at large (Glancey and Pettygrew 1997; Harris and Watkins 1998). Finally, Wanhill (1997) suggests that such additional weaknesses associated with small and medium-sized tourism enterprises as: (i) supply dominated by the family business; (ii) a lack of commercial drive and initiative, owing to non-economic motives for operating in the business; and (iii) limited skills of marketing, quality, assurance, pricing policy, cost control and re-adjustment and a shortage of financial resources, highly affect the efficiency of such enterprises. Consequently, such characteristics should be taken into consideration when analyzing the efficiency of small hotels. Following Farrell (1957), frontier estimation techniques represent technology by a bounding function that reflects best-practice production; defined in terms of the maximum real output technologically possible to produce given available inputs. In general, there are two types of hotel benchmarking: (1) the internal method, conducted amongst different units of the same chain and adopted by Barros (2004), Barros and Alves (2004) and the present study; and, (2) the external method, based on comparing different enterprises within the same sector and adopted by Anderson et al. (1999a; 1999b; 2000), Morey and Dittman (1995) and Bell and Morey (1995). In addition, there are two scientific methods used to analyze efficiency quantitatively: the econometric stochastic frontier approach and data envelope analysis (DEA). Both methods have their advantages and drawbacks. Unlike the econometric stochastic frontier approach, DEA permits the use of multiple inputs and output, and does not impose any functional form on the data nor does it make distributional assumptions for the inefficiency term. However, both methods assume that the production function of the fully efficient decision unit is known; in practice, this is not the case as the efficient isoquant must be estimated from the sample data. Under these conditions, the frontier is, therefore, relative to the sample considered in the analysis. The econometric stochastic frontier also presents some important advantages, such as a number of well-developed statistical tests to investigate the validity of the model specification – tests of significance for the inclusion or exclusion of factors, or for the functional form. The accuracy of this hypothesis, however, depends to some extent on the assumption of the normality of errors, which is not all times fulfilled. Another advantage of the econometric frontier is that if a variable which is not relevant is included, it will have a low or even zero weighting in the calculation of the efficiency scores; so, its impact is likely

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109

to be negligible. This is an important difference from DEA, in which the weights for a variable are usually unconstrained. A third advantage is that it allows the decomposition of deviations from efficient levels between “noise” (or stochastic shocks) and pure inefficiency, whereas the DEA classifies the whole deviation as inefficiency. In this paper, we undertake a benchmarking exercise of a Portuguese hotel chain comprised of small size hotels, in order to estimate the efficiency scores at the econometric production frontier. Frontier models have been applied in the study of tourism by Anderson et al. (1999a; 1999b; 2000), Morey and Dittman (1995) and, Bell and Morey (1995), to measure efficiency and enforce organizational arrangements that minimize costs. This paper contributes to the extant literature on small hotels by estimating a production function for a sample of small hotels, whereas previous research using frontier models does not consider small hotels, nor does it analyze production frontiers. The adequacy of production functions is derived from the competitive nature of the market in which the hotels operate (Khumbhakar 1987), whilst cost models are more appropriate for oligopolistic markets. In addition, this paper contributes to tourism research by estimating a production function allowing for contextual characteristics in a simultaneous two-stage procedure (Battese and Coelli 1995); this stands in contrast to previous research, in which the analysis of contextual variables relied on DEA or on econometric cost functions (Anderson, Fish, Xia and Michello 1999). Finally, the motivation for this research stems from the need to understand specific aspects of small hotel efficiency, which differentiate it from generic efficiency analysis according to the literature (Wanhill 1997).

2 Contextual Setting In 1998, tourism in Portugal accounted for 1.1% of the total global tourism while its neighbor, Spain, accounted for 6.7% (see The Financial Times Survey on Portugal, 21 October 2002). This small value for tourism in Portugal at global level stands in sharp contrast to the significance of the tourist industry at the national level, where the industry represents an estimated 5% of the GDP, with Spanish demand accounting each year for over 60% of total demand (Matias 2004). Considering the national level significance for Portugal of its tourist industry the case for addressing issues of efficiency within its hotel industry builds almost for itself, particularly on what concerns small

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hotels which, as a result of low (or rather absent) economies of scale, face a lack of competitive advantage. In addition, small hotels cater to discerning tourists who seek in their Portuguese experience an atmosphere far removed from what is offered by the large international hotel chains (Santos 2004). The small hotels analyzed in this paper are managed by a holding company named Enatur. These hotels have various characteristics which distinguish them from other chains, associations or forms of accommodation available in the Portuguese market. The primary distinction is that the chain, indeed, the particular category of hotel, was created by the Portuguese state, but has recently been partially privatized. The concept behind the Enatur hotels, the first of which opened in 1942, is to provide comfortable, rustic, genuinely Portuguese lodging in locations of outstanding historic or scenic merit, while restoring and preserving Portuguese cultural, historic and architectural heritage. The cuisine at all of today’s forty-four hotels highlights traditional dishes and wines of the regions in which they are situated. The hotels are found outside densely populated dimensions and stand apart from the masstourism destinations. Each has its own unique characteristics, ranging from the moderate to the luxurious, and with a limited number of rooms (the smallest has only nine rooms and the largest has fifty-one). Enatur has developed two branches of the hotel chain: the historic and the regional. The 15 historic hotels are situated in carefully restored monuments, mainly castles, monasteries and convents, adapted to the needs of the modern hotel industry. The 28 regional hotels are purpose-built with its architecture respectfully blending into the local environment, in locations of great natural beauty or historic interest. The most recent hotel to be constructed opened to guests in 2002, so that there is no data available yet. However, since this is being operated under franchising, and therefore breaking with tradition, it will not be analyzed in this study. Summarizing, these small Portuguese hotels are not necessarily on par with the best or the most comfortable hotels nor with those which offer a complete range of services; however, they possess their own identity, catering for the discerning tourist who seeks a memorable experience and atmosphere which is far removed from what is offered by the large international hotel chains in Portugal. Some characteristics of the Enatur hotels analyzed are represented in Table 1.

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The hotel chain is dispersed throughout the whole of mainland Portugal; the clientele breakdown for 1999 shows that 49% of the guests were Portuguese and 51% were international. Among the latter, the strongest representation (17.2%) was from Germany, followed by the USA with 15.7%, then the UK with 10.4%. As a public enterprise committed to the restoration and conservation of Portuguese heritage, as well as the promotion of tourism, Enatur is confronted with conflicting objectives. The hotels are small, with small scale economies and a high level of debt due to the high cost of restoring and modernizing buildings sometimes hundreds of years old. Moreover, they are dispersed Table 1. Characteristics of the hotel sample used in the analysis, 2001

Location

Rooms

Condeixa 44 Sagres 39 Santiago do Cac´em-Quinta 13 da Ortiga Bragan¸ca 28 Viana do Castelo 48 ´ Evora 32 S˜ ao Br´ as de Alportel 33 Arraiolos 32 Murtosa 19 Manteigas 21 Gerˆes 29 Alc´ acer do Sal 35 Crato 24 Guimar˜ aes-Santa Marinha 51 Vila Nova de Cerveira 28 Queluz 26 Beja 35 Serpa 18 Batalha 21 Marv˜ ao 29 Torr˜ ao 14 Alij´ o 21 Vila Vi¸cosa 36 Santiago do Cac´em-S˜ao 9 Tiago

Classification Location Dimension (Historic or (coast=1, (square Regional) interior=0) meters) R R R

0 1 1

1393 1959 2347

R R H R H R R R H H H H H H R R R R R H R

0 0 0 0 0 0 0 0 1 0 1 1 1 0 0 1 0 0 0 0 1

1632 1778 2622 1089 3495 600 644 970 3009 3214 3804 2632 1245 3120 1019 1068 1181 768 754 3904 616

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´ Carlos Pestana Barros and Alvaro Matias Table 1. (Cont.)

Location

Rooms

Classification Location Dimension (Historic or (coast=1, (square Regional) interior=0) meters)

´ Obidos Santa Clara a Velha Monsanto Alvito Guimar˜ aes-Nossa Senhora da Oliveira Sousel Palmela Caramulo Miranda do Douro P´ ovoa das Quartas Estremoz Elvas Our´em Set´ ubal Mar˜ ao Valˆen¸ca do Minho Amares Castelo de Bode Almeida Mean value

9 19 10 20 16

H R R H R

1 1 0 0 1

583 841 344 1809 392

32 28 12 12 16 33 25 30 16 15 18 32 25 21 25

R H R R R H R R H R R H R R –

0 0 0 0 0 0 0 0 1 0 0 0 0 0 –

3170 1881 726 820 1386 2070 1600 1000 2077 704 992 1663 1138 1331 1.614

Source: Enatur’s Control Reports from 1999 to 2001

throughout the country, and in some cases situated in destinations where there is no other tourist infrastructure. Such characteristics must be taken into account when analyzing factors which affect the performance of the chain.

3 Literature Review Extant analysis of hotel efficiency is restricted to a small number of studies. Among the earliest studies, we cite Baker and Riley (1994) who suggest the use of ratios to analyze the performance of the lodging industry, Wejeysinghe (1993) who suggests the use of break-even analysis to analyze the effectiveness of tourism management, and Brotherton and Mooney (1992), as well as Donaghy (1995), who suggest yield

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management to analyze the efficiency of hotel management. Among papers adopting more contemporary techniques we may find Bell and Morey (1995), who studied 31 travel departments with the help of DEA, by estimating their efficiency ratios. They used four inputs: (1) actual levels of support costs (fees, labor, space, technology, etc.), (2) actual levels of expenditure on travel (hotel, flight, and car rental charges), (3) level of environmental factors (means of negotiating discounts, percentage of trips with commuter flights required) and (4) nominal levels of other expenditures. They chose as outputs the level of service provided (excellent and average). Anderson et al. (1999b) dissected the efficiency of 31 travel management departments, with the use of DEA and a stochastic frontier. They operated nine inputs: (1) fee expenditure, (2) car expenditure, (3) technology costs, (4) labor expenditure, (5) hotel expenditure, (6) hourly labor costs, (7) part-time labor costs, (8) total air travel expenditure, and (9) building and occupancy expenditure. They defined output by the number of trips. To convert inputs into prices, they divided the three following input categories by the number of trips. To estimate the price of labor, they divided labor expenditure by the number of trips. To assess the price of travel, they divided travel expenditure by the number of trips. To reckon the price of capital, they divided capital expenditure by the number of trips. Other papers analyzing hotel efficiency are: Morey and Dittman (1995), who analyzed 54 USA hotels with a DEA model. The outputs adopted were room division expenditure, energy costs, salaries, nonsalary expenditures, salaries and related expenditure for advertising, fixed market expenditure for administrative work. The inputs adopted were total revenue, level of service delivered, market share and the rate of growth. Johns, Howcroft and Drake (1997), who analyzed 15 UK hotels with DEA. The inputs used were number of room nights available, total labour hours, total food and beverage costs and total utilities costs. The output used were the number of room nights sold, total covers served and total beverage revenue. More in line with the present research, Anderson, Fish, Xia and Michello (1999) analyzed 48 USA hotels with a stochastic production translog frontier. Total revenue is regressed in the prices and inputs. The prices were the price of labor proxied by the hotel revenue per full-time equivalent employee, room price proxied by the hotel revenue by the product of the number of rooms times the occupancy rate and

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day per year, and the price of gamming, food beverage and other expenses proxied as the percentage of total revenue. The inputs were the number of full-time equivalent employees, number of rooms, total gaming-related expenditures, total food and beverage expenses and other expenses. Anderson, Fok and Scott (2000), applied a DEA technical and allocative model to 48 USA hotels. The inputs used were the full-time equivalent employees, the number of rooms, total gaming-related expenses, total food and leverage expenses and other expenses. The outputs used were total revenue and other revenue. The prices used were wages proxied by the hotel revenue per full time employee, the rooms price proxied by the hotel revenue divided by the product of rooms times occupancy rates and day per year. Brown and Ragsdale (2002), analyzed the efficiency of 48 USA hotels with a DEA-CCR model and a cluster analysis. The data were individual questionnaire data. The inputs used were the median price, problems (defined in a 4-point scale), service quality, upkeep, hotels and rooms. The outputs were satisfaction value (defined on a 100-point scale) and value (defined in a 5-point scale). Hwang and Chang (2003), who analyzed the efficiency of 45 Taiwan hotels with three DEA models (CCR, super-efficiency model and Malmquist model). The inputs used were the number of full-time employees, total number of guestrooms, total dimension of meal department and operating expenses. Reynolds (2003) who analyzed the efficiency of 38 USA restaurants. The inputs used were front-of -the-house hours worked per day during lunchtime, front-of–the-hours worked during dinner per day, average wages. Uncontrollable inputs were used, namely, number of competitors with a two-mile radius and the seating capacity. The outputs were sales and consumer satisfaction. Barros and Alves (2004), who analyzed 42 Portuguese small hotels with a DEA-Malmquist index. The inputs used were full-time workers, cost of labour, book value of property, operating costs and external costs. The outputs were sales, number of guests, night spent in the hotel. Barros (2004), analyzed the same hotels with a stochastic CobbDouglas cost frontier model. The operational cost was regressed in prices and outputs. The prices were the value of sales and the number of nights slept. The input prices were the price of labour, price

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of capital and price of food. Chiang, Tsai and Wang (2004) analyzed 25 Taipei hotels with two DEA models (CCR and the BCC model); Barros and Mascarenhas (2005) analyzed 43 Portuguese small hotels with a DEA-Allocative model; Barros (2005A and 2006) analyzed the efficiency of Portuguese hotels with various efficient methods. We verify that the research using frontier models applied to tourism is enlarging and encompassing the traditional models used in this methodological issue. When comparing this research with that undertaken in other fields, it should be considered that this is one of the main fields in economics in which frontier models have been applied, with such diverse methods that range from DEA to econometrics, thereby revealing openness to different approaches that we do not see in other fields. The general conclusion that emerges from the research described above is that dimensions are important (Anderson, Fish, Xia and Michello 1999), that location is also important (Anderson, Lewis and Parker 1999) and that public ownership seems to constitute a significant disadvantage in relation to efficiency (Barros, 2004, 2005a). Moreover, action intended to improve the rate of total productivity growth is to be welcomed, as long as it is focused on capital accumulation and the rate of innovation to shift the frontier of technology, i.e. technical change (Barros and Alves, 2004). Price efficiency is also a major issue (Barros and Mascarenhas, 2005; Anderson, Fok and Scott, 2000). Finally, quality perception is important (Brown and Ragsdale, 2002). What are the policy implications of these reviewed studies? They vary, but in general they propose policies to overcome the identified inefficiency. When compared with other research fields, this bibliography is, in our view, clearly brief for such an important issue in the tourism market context. With the present paper, we seek to enlarge the economics of tourism in this specific respect and to call the attention of other tourism researchers to this neglected aspect of tourism management.

4 Measuring Productive Efficiency This paper adopts the stochastic production econometric frontier approach. The frontier approach, first proposed by Farrell (1957) and based on cost functions, came to prominence in the late 1970s as a result of the work of Aigner, Lovell and Schmidt (1977), Battese and Corra (1977) and Meeusen and Van den Broeck (1977). The adequacy of cost or production function depends on the environment in which

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the units analyzed exist. In an environment in which the ultimate objective is to maximize sales and profits, the producers face exogenously determined input and output prices and attempt to allocate inputs and outputs so as to maximize sales. We assume that this is the main strategy in hotels and, therefore, adopt the production econometric frontier. Frontier models with production function are common in banking, but rare in other literature. In a production frontier, the units on the frontier are efficient and the units below it are inefficient. The zone above the frontier is unattainable, since the most production-efficient unit is on the frontier. For reading purposes, frontier models normalize the efficient scores in order for them to be equal to one for the best performing hotels and less than one for the inefficient frontier. The econometric frontier estimates the units above the frontier econometrically and measures the difference between the inefficient units and the frontier by the residuals. This is an intuitive approach. However, when we assume that the residuals have two components (noise and inefficiency), we have the stochastic frontier model. Therefore, the main issue in econometric frontier models is the decomposition of the error terms. The general frontier production function, dual to the cost function proposed by Aigner et al. (1977) and Meeusen and Van den Broeck (1977), is the following: Ynt = Y (Xint ).eε

i, j = 1, 2, . . . N; t = 1, 2, . . . N.

εnt = Vnt −Unt , (1)

where Ynt represents a scalar production (sales) of the n-th hotel in the t-th period; Xint is a vector of ith-input measurements (labour and capital) of the n-th hotel in the period t. ε is the error term. The error term is composed of Vnt which the traditional error term of econometric models assumed to be independently and identically distributed, which represents the effect of random shocks (noise) and is independent of Unt . The inefficient term Unt represents technical inefficiencies and is assumed to be positive and distributed normally with zero mean and variance σU2 . The Unt positive disturbance is reflected in a half-normal independent distribution truncated at zero, N (Unt, σU2 ), signifying that each hotel’s production must lie on or above its cost frontier, as well as above the level of one. This implies that the two effects, the V effect, which is a random shock, and the U effect, which is a management shock controlled by the office, cause any deviation from the frontier. The mean inefficiency of the technical efficiency effects model, in Coelli et al. (1998) is a deterministic function of p explanatory variables: Unt = znt δ ,

(2)

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where δ is a p × 1 vector of parameters to be estimated. Following Battese and Corra (1977), the total variance is defined as σ 2 = σV2 +σU2 . The contribution of the error term to the total variation is as follows: σV2 = σ 2 /(1 + λ2 ). The contribution of the inefficient term is as follows: σU2 = σ 2 λ2 /(1 + λ2 ), where σV2 is the variance of the error term V , σU2 is the variance of the inefficient term U , and λ is defined as λ = σσVU , providing an indication of the relative contribution of U and V to ε. The inefficiencies in Unt in (1) can be specified as: Unt = Znt δ + Wnt ,

(3)

where Wnt is defined by the truncation of the normal distribution with mean zero and variance σ 2 . Using this parameterization, a test can be run to determine whether the estimated frontier is actually stochastic; λ = 0 implies that the variance associated with the one-sided (efficiency) errors, σU2 , is zero, meaning that these deviations from the frontier are better represented as fixed effects in the production function. Therefore, a test of the null hypothesis that λ = 0 against the alternative hypothesis that λ is positive is used to test whether deviations from the frontier are stochastic and whether one should proceed with the estimation of parameters related to the sources of inefficiency within the context of a stochastic production frontier. Failure to reject the null hypothesis suggests that the determinants of inefficiency, Zit should be included in the cost function. The parameters of the model (β, δ, σ and λ) are estimated using the maximum-likelihood estimator; the likelihood function can be found in Battese and Coelli (1988). Thus, the technical inefficiency of the i-th club at time t is: T Ent = ex(−Unt ) = exp(−znt δ − Wnt ) .

(4)

The conditional expectation of TE is defined under the half-normal assumption: ⎤ ⎡ μ∗n ⎢ φ( σn∗ ) ⎥ ∗ ∗⎢ ⎥, (5) E [Ui /εn1 , . . . εnt ] = μn + σi ⎣ μ∗n ⎦ φ(− ∗ ) σn  2 σU λ ∗ ∗ where μn = γn μ + (1 − γn )(−εn ), γn = 1/(1 + T n ) and σn = ( 1+λT n ). μ is the mean value of the distribution and T is the time period of the panel, φ is the standard normal distribution, and φ is the respective cumulative distribution function (coelli, Rao and Battese 1998; Kumbhakar and Lovell 2000).

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5 Data Collection and Analysis To estimate the production frontier, we use a balanced-panel database from Enatur’s Control Reports from 1999 to 2001 (3 years × 42 hotels 126 observations), containing information on both inputs and outputs. Frontier models require the identification of inputs (resources) and outputs (transformation of resources). Several criteria can be used in their selection. One empirical criterion is availability. The literature survey is also a way to ensure the validity of the research and therefore, another criterion to take into account. Finally, the last criterion for measurement selection is the professional opinion of managers. In this paper, we follow the first two criteria. The hotels used in the analysis and their economic characteristics were presented above in Table 1. Table 2 presents the characteristics of the variables used in the analysis. We transformed the variables according to the description column in Table 2. We adopt the traditional log-log specification to allow for the Table 2. Descriptive statistics of the data 1999–2001

Variable Log (Sales)

Description

The logarithm of sales at constant price 1999=100 Log (Labor) Logarithm of the number of equivalent employees Log (Capital) Logarithm of the book value of premises Trend Trend variable, aiming to capture time effects on the data, which are independent of the constant. Dummy (His- Dummy (one for toric) historic hotels and zero for regional hotels) Log (Dimension) Square meters of the hotel premises

Minimum Maximum Mean Standard deviation 5.1

6.4

5.823 0.268

1.1

1.7

1.403 0.158

4.1

6.9

6.010 0.638

1.0

3.0

2.000 0.819

0.0

1.0

0.357 —

2.5

3.6

3.142 0.266

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possible non-linearity of the frontier. In the production function, the dependent variable Sales is regressed in the trend, in the inputs labor and Capital, and in the inefficient variables Historic and location. These inefficient variables were selected based in the discussion of the contextual setting. We verify that the range is narrow, indicating that the hotels in the sample are of similar dimension in terms of inputs and outputs; confirming the results of Table 3. The contextual variables outside the control of the management are the dummy variable for classification (historic vs. regional), and the dummy variable for location (coast versus interior). The dummy, historic is an internal fixed characterization; we assume it to be a contextual characteristic but under the management’s control. The dummy, location is a contextual variable beyond the management’s control. The rationale for including this variable is based on the geographic division of the country into a coastal zone, in which is concentrated the majority of the population, and a relatively undeveloped interior. While we acknowledge that tourists are not locals, a remote location is recognized to be a weakness of small hotels (Glancey and Pettygrew 1997). Table 3. Stochastic Cobb-Douglas panel production frontier

Variables

Coefficients (t-ratio)

Constant (β0 )

−1.918 [−16.993]∗

Log Labor (β1 ) Log Capital (β2 )

0.097 [8.263]∗ 0.012 [3.729]∗

Trend (β3 ) Constant (δ0 )

0.528 [3.618]∗ −0.277 [−2.785]∗

Historic (δ1 ) Location (δ2 )

0.212 [9.993]∗ −0.006 [−0.458]

Variance parameters σs2 = σu2 + σv2 σ2 γ = u2 σs Log(likelihood) LR test Observations

0.306 [6.090]∗ 0.058 [5.111]∗ 206.785 107.142 126

t Statistics in parentheses are below the parameters, those followed by ∗ are significant at 1% level.

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6 Results The Translog model was initially chosen on the basis of the number of degrees of freedom – considering 126 observations and 6 exogenous variables – but was later abandoned on observing the statistical results. A Cobb-Douglas model was then chosen and statistically supported by the data. LogSalesnt = β0 + β1 log Labournt + β2 log Capitalnt + β3 T rendnt + (Vnt − Unt ) Unt = δ0 + δ1 Historicnt + δ2 Locationnt .

(6)

This model is the production frontier model, known as the Technical Efficient Effects Model (in Coelli et al. 1998), because it accounts for causes of efficiency due to the enterprise (Labor and Capital) and to the contextual characteristics not directly under the control of the management (Historic, Dimension and Location). The variables have been defined and characterized in Table 3. The time trend aims to approximate technical change. Table 3 presents the results obtained for the stochastic frontier. We verify that the Cobb-Douglas production function specified above fits the data well, as the R-squared from the initial ordinary least-squares estimation that was used to obtain the starting values for the maximum-likelihood estimation is in excess of 93.6% and the overall F-statistic is 153,76. As the null hypothesis is formulated in terms of the absence of a one-sided error term (i.e. the errors consist only of normally distributed random errors), the fact that the LaGrange test statistic of 107,142 (critical value equal to 2.5) is significant confirms that we can specify and estimate a stochastic frontier analysis using the Cobb-Douglas production function. We also verify that the variables have the expected signs, with the productions increasing with the quantity of capital and the quantity of labor as well as the trend variable. Relative to the frontier parameters, it is verified that σ2 is statistical significant meaning that the standard deviation of the sum of the error terms is statistical significant, which signifies that estimating this data with a standard production function will result in inefficient estimates. Moreover the λ is also significant and equal to 5.8%, which means that the inefficient term is 5% of the total error. This is a small, but significant value.

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Regarding the contextual variables, the coefficients δ are the inefficient coefficients so a positive coefficient indicates a negative impact on efficiency. Therefore, the sales decrease with the historic classification, which signifies that this segmentation is not related to efficiency. However, the sales increases when the location is near the coast, which means that to be located in more crowded dimensions is a condition to attract more clients to such hotels. However, the location dummy is statistically insignificant, and therefore this conclusion is limited. Finally, the value of parameter γ is positive and statistically significant in the stochastic inefficiency effects. Therefore, the traditional production function with no technical inefficiency is not an adequate representation of the data.

7 Managerial Implications Table 4 presents the results of the efficiency scores computed from the residuals for the years 1999–2001. Technical efficiency is achieved, in a broad economic sense, by the unit which allocates resources without waste; thus, the concept refers to a movement towards, or away from, the best-practice production frontier activity. A movement towards this sort of production is an improvement, while a movement away from it represents deterioration. The efficient scores were calculated according to (7) T Eit =

Xit β + (Vit − Uit ) . Xit β + Vit

(7)

Equation 7 measures technical efficiency scores as the production of the hotel relative to the ideal, or best-practice production function, using the same input mix. The estimation of the nominator needs the decomposition of the individual residuals into their component parts, which is done with the Jondrow, Lovell, Materov, and Schmidt (1982). As shown in below, the mean score is high and fluctuates in the period. This score suggests that hotels are on the frontier of the best practices. The maximum unit efficiency score, which is naturally 1, is observed only for the two hotels: Obidos and Queluz. The minimum efficiency score was 0.8106 in 1999, 0.8136 in 2000 and 0.9080 in 2001. The median was 0.9775 in 1999, 0.9825 in 2000 and 0.9835 in 2001. The standard deviation was 0.0560 in 1999, 0.0566 in 2000 and 0.0333 in 2001. They are average in comparison with what is found elsewhere in

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´ Carlos Pestana Barros and Alvaro Matias Table 4. Small hotels’ efficiency scores

Location

1999

2000

2001

´ Obidos Queluz Alc´ acer do Sal Alvito Amares Vila Vi¸cosa Vila Nova de Cerveira Beja Crato Estremoz Arraiolos ´ Evora-loios Guimar˜ aes-Santa Marinha Palmela Set´ ubal Almeida Batalha Caramulo Bragan¸ca Castelo de Bode Condeixa Elvas Gerˆes Guimar˜ aes-Nossa Senhora da Oliveira Manteigas Mar˜ ao Marv˜ ao Miranda do Douro Monsanto S˜ ao Tiago do Cac´em-S˜ao Tiago Alij´ o Murtosa Santa Clara a Velha Torr˜ ao Valˆencia do Minho P´ ovoa das Quartas Serpa S˜ ao Br´ as de Alportel

1.000 1.000 0.997 0.995 0.994 0.981 0.983 0.992 0.990 0.989 0.993 0.987 0.984 0.983 0.985 0.980 0.979 0.978 0.978 0.977 0.977 0.976 0.964 0.953 0.948 0.947 0.920 0.937 0.923 0.885 0.980 0.912 0.844 0.834 0.820 0.910 0.852 0.910

1.000 1.000 0.998 0.997 0.996 0.997 0.997 0.995 0.996 0.990 0.994 0.989 0.993 0.989 0.988 0.985 0.984 0.983 0.983 0.982 0.982 0.981 0.969 0.958 0.952 0.951 0.924 0.941 0.927 0.889 0.993 0.916 0.847 0.837 0.823 0.914 0.855 0.914

1.000 1.000 0.999 0.998 0.997 0.997 0.997 0.996 0.996 0.996 0.996 0.995 0.994 0.991 0.99 0.987 0.986 0.985 0.985 0.984 0.984 0.983 0.980 0.970 0.965 0.964 0.963 0.962 0.961 0.957 0.946 0.941 0.940 0.939 0.936 0.932 0.925 0.924

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Table 4. (Cont.)

Location

1999

2000

2001

Viana do Castelo Sagres S˜ ao Tiago do Cac´em-Quinta da Ortiga Sousel Mean Median Std. Dev.

0.810 0.898 0.879 0.852 0.944 0.977 0.056

0.813 0.902 0.882 0.855 0.949 0.982 0.056

0.908 0.896 0.893 0.880 0.967 0.983 0.033

the same industry and higher than the results in other industries such as banking and insurance. With regard to the significance of these results, clearly, they are mixed, but overall, we verify that the efficiency scores are time varying according to the trend parameter – signifying that the latter fluctuates. Moreover, almost half of the hotels have higher efficiency scores than the median –signifying that the sample has an almost normal distribution. However, as the median is smaller than the mean, the majority of hotels present an efficient score lower than the mean. We also verify that the most efficient hotels are situated on the outskirts of Lisbon, the capital and largest city in Portugal, while the least efficient hotels are situated in isolated areas of the country. The general conclusion is that the Enatur management is improving the hotels’ efficiency, but structural limitations exist for the hotels with the poorest performance, since they are in locations which are more isolated and consequently, cannot benefit from the same potential flow of customers as those in the towns and dimensions which are more densely populated. Based on the paper results, we emphasize three managerial implications for our findings. First, the group management should improve its procedures by adopting an efficient, enhanced-incentive policy, which would enable the inefficient hotels to catch up with the efficient frontier. Second, the adjustment must be based on the improvement of technical efficiency. Thirdly, in order to maximize production efficiency, hotel managers should control labor and capital and aim to increase the number of nights that guests stay based in their discretionary power. Moreover, they should adapt to their contextual setting in order to increase production efficiency. In the contextual variables, we observe that the historic classification is negatively and statistically significantly related to sales, signifying that this segmentation classification has no market

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value. Moreover, the situation of the hotels near the coast is not a condition to be efficient, since this parameter is also negative, but statistically insignificant. Finally, the dimension of the hotel has a positive effect on sales, which is statistically significant; therefore in this context small dimension is not an advantage. Since we analyzed technical efficiency, it was necessary to look for the causes of technical inefficiency underlying the estimated model. Various causes, endogenous to the hotels analyzed, can be advanced to account for such performance differentials. Technical efficiency is, in a broad economic sense, about a unit allocating resources without waste and refers to a movement towards the best-practice production frontier function. Technical inefficiency of small hotels, according to the literature (Glancey and Pettygrew 1997; Harris and Watkins 1998; Wanhill 1997), is a consequence of one or more causes: Firstly, structural limitations related to planning, such as lack of economies of scale and scope and peripheral location. In the Cobb-Douglas production function the scale is the sum of the parameters, which are clearly higher than unity3 , signifying economies of scale, despite the small dimension of the hotels analyzed. Secondly, low-grade management practices, deriving from a lack of entrepreneurial drive, which are often due to an assortment of different causes, can only be analyzed in situ. This absence of good practices is usually related to the mismanagement of the resources (financial, physical, human, legal, organizational, informational, relational and cultural), but the data set does not allow us to engage in inference on this regard. Finally, structural restrictions inherent to the organizational management are also to be mentioned, such as: (1) the principal-agent relationship (Jensen and Meckling 1976). This relates to the difficulty of controlling those empowered as managers to act on behalf of the owner and is one of the more prevalent issues in dealing with efficiency – particularly in public enterprises; (2) structural rigidities associated with the labor market, which give rise to the collective-action problem (Olson 1965), and allow employees to “free-ride” on the management’s own efforts to improve performance. This occurs when job tenure is not linked to performance but is overly dependent on laws that protect the employees; (3) an alternative structural restriction that causes waste in management by providing unequal access to information on operational activities; that is, asymmetric information distribution (Williamson 1975; 1998) among the hotels in 3

We do not display this value, but the reader can immediately verify it.

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the chain, due to the spatial dispersion of the hotels and the agglomeration context in which the hotels exist. If some of the hotels enjoy more privileged access to information than others, then the former will have the opportunity to attract more clients than the latter; and, (4) organizational factors associated with X-efficiency (Leibenstein 1966). These factors are derived from incomplete markets. Incomplete markets exist everywhere, but are prevalent in small hotel chains where different tasks are allocated to the central management and to the unit manager. In this situation, the management may be unable to adopt the correct strategy, since it may not know what it should be. We are unable to test bad management practices and structural restrictions with the present model, and therefore only cite them as possible causes of the performance differentials observed.

8 Contribution, Limitations and Extensions of This Study In the light of the extensive literature on productivity in tourism, it is useful to consider the potential contributions of the current research. Based in our literature survey, we estimate a stochastic frontier model for hotel-chain efficiency. Moreover, we adopt the Technical Efficient Effects model proposed by Coelli et al. (1998) which accounts for efficiency due to the enterprise, as well as the contextual setting outside the firm. Finally, our stochastic production frontier model lends support to similar works (Anderson, Fish, Xia and Michello 1999), but it is worth noting that our construct has a stronger theoretical foundation, since it takes into account contextual variables. In term of limitations of the study, the homogeneity of the hotels used in the analysis is questionable, since we compare units with different dimensions. However, we posit that the units are not comparable and therefore, a ratio analysis could not be carried out. Moreover, the data set is short, thus the conclusions are limited. In order for them to be generalized we would need to have a panel data set with a larger span. Finally, a variety of extensions to this study can be undertaken. First, non-parametric free-disposal hull analysis can be used to assess the efficiency scores. However, previous research has shown that while the DEA scores are inferior in value to econometric scores, the ranking is preserved when the same variables are used (Bauer et al. 1998). Second, the comparisons with different chains acting in the same market, and with other European hotels acting in different markets, would be the road ahead for benchmarking purposes.

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9 Conclusion This article proposes a simple framework for the evaluation of small hotels and the rationalization of their operational activities. The analysis is based on a production frontier model that allows for the incorporation of multiple inputs and outputs in determining the relative efficiencies. Benchmarks are provided for improving the operations of the more poorly performing small hotels. Several interesting and useful managerial insights and implications from the study are raised. The general conclusion is that the majority of the small hotels analyzed are efficient, while a proportion of them are revealed to be inefficient when the benchmark used is the mean or the median. Overall, the results suggest that capital, labor and nights slept are the main determinant factors of efficiency in this sector. Inputs are negative determinants and nights slept are positive determinants. Also, important contextual variables, which are also positive determinants of efficiency in the hotel chain studied, are historic classification and the location. Negative contextual variables, on the other hand, are the dimension of the hotel. As such, a management policy to improve efficiency should take into account these results while further investigation is needed to address the limitations mentioned.

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Part II

New Operational Tools in Tourism Research

Destination Competitiveness: Meeting Sustainability Objectives Through Strategic Planning and Visioning Lisa Ruhanen The University of Queensland, The School of Tourism and Leisure Management, 11 Salisbury Road, Ipswich, QLD, 4305, Australia [email protected]

1 Introduction Destinations, whether national, state or local, are increasingly taking their role as tourist destinations very seriously as evidenced by the allocation of considerable funds towards tourism promotion and marketing. Much of this funding is being directed towards the enhancement and development of their touristic image and attractiveness (Ritchie & Crouch, 2000). With so many destinations competing for the tourist dollar, both on an international and domestic level, competition is fierce and destinations are looking to capitalize on all of their assets to differentiate themselves from their competitors. This increasing level of competition has highlighted the need for destination planners and managers to adopt a more strategic focus to ensure this share is sustained in the future (Faulkner, 2003; Ritchie & Crouch, 2000). As Ritchie (1999:273) notes, “the growing level of international competition in the tourism marketplace, and the length of lead times for developing the major facilities necessary to meet this competition, have made strategic level planning increasingly imperative”. A destination’s competitiveness has traditionally been viewed in economic terms. However, the advent of triple–bottom line accounting in both the public and private sectors has placed increasing pressure on destination planners and managers to give due consideration to the sustainable management of the environmental and social resources of the destination. This has further highlighted the need for tourism destinations to proactively and strategically plan for future developments in order to secure, both the long–term sustainability of the destination

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and continuing market competitiveness, as the cumulative effects of today’s development decisions will have impacts well beyond the lifetimes of those making the decisions (Ritchie, 1999). While the notion of destination strategic planning itself is not new, incorporating the requirements of sustainability into the planning process has proven to be more of a challenge (Ruhanen, 2004). To address this cases are emerging where the strategic planning methodology has been revised to incorporate a ‘strategic visioning’ phase with the objective of developing a vision for the future of the destination. A recognized contributor to successful organizations in the private sector, the formulation of a destination vision aims to develop consensual guidelines on appropriate forms of development which are consistent with the values and goals of the destination community, while taking into account the opportunities and constraints of the market (Ritchie & Crouch, 2000). This paper will examine the notion of destination competitiveness and the more recent incorporation of sustainability objectives into this field of research. To meet these objectives the strategic planning and visioning approaches to tourism destination planning will be discussed. The appropriateness of these approaches will be conceptually critiqued in light of their contribution towards sustainable development goals and destination competitiveness.

2 Destination Competitiveness Increasing globalization and the shift from commodity–based economies towards economies driven by knowledge development, innovation and commercialization, have seen the issue of competitiveness rise in importance. Competitive advantage is considered imperative to the success of organizations, whether private or public, and for regions, states and countries (Porter, 1980). It is an issue that is receiving increasing attention from industry, government and academia and is an international phenomenon, not restricted to any particular countries (Kim & Dwyer, 2003). As with other fields, competitiveness has become a key area of focus in the tourism destination literature, with research focusing on such factors as destination attributes, image, price, consumer preferences and perspectives of experience, quality and performance (see Ahmed, 1991; Dwyer, Forsyth & Rao, 2000; Goodrich, 1977; Haahti & Yavas, 1983; Kim & Dwyer, 2003; Kozak & Rimmington, 1999; Laws, 1995; Pearce, 1997). Similarly much emphasis has been placed

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on the development of models and tools for enhancing and measuring a tourism destination’s competitiveness (see Bordas, 1994; Evans, Fox & Johnson, 1995; Faulkner, Opperman & Fredline, 1999; Hassan, 2000; Kim & Dwyer, 2003; Ritchie & Crouch, 2003). Tourism destination competitiveness can be defined as “a general concept that encompasses price differentials coupled with exchange rate movements, productivity levels of various components of the tourist industry and qualitative factors affecting the attractiveness or otherwise of a destination” (Dwyer, Forsyth & Rao, 2000: 9). It can also be said that destination competitiveness is the ability of a destination to deliver goods and services that perform better than other destinations on those aspects of the tourism experience considered to be important by tourists (Dwyer & Kim, 2003). The destination’s resources are undoubtedly an important source of comparative and competitive advantage in tourism. “Comparative advantage would relate to climate, scenery, flora, fauna, etc., while competitive advantage would relate to such items as the tourism infrastructure (hotels, events, attractions, transport networks), the quality of management, skills of workers, government policy etc.” (Kim & Dwyer, 2003: 58). Buhalis (2000:106) adopts a slightly broader perspective and defines destination competitiveness as including “the sustainability of local resources for ensuring the maintenance of long–term success as well as the achievement of equitable returns–on–resources utilized to satisfy all stakeholders”. Ritchie and Crouch (2000) support this view and note that competitiveness is illusory without sustainability and that to be truly competitive, a destination’s tourism development must be economically, ecologically, socially, culturally and politically sustainable. A destination’s competitiveness and success in the tourism marketplace has traditionally been measured in economic terms, and while this is likely to continue to be the most effective and prominent indicator, triple bottom line accounting is increasingly forcing attention towards the environmental and social aspects of the destination. Entering the sustainability concept into the discussion on destination competitiveness reflects the recognition that economic issues cannot be given undue precedence over environmental and social issues. As Mihaliˇc (2000) reflects, tourism planners and managers have generally only been willing to incorporate environmental measures into management strategies if they resulted in lower costs and/or higher profits. However, consumers increasing environmental consciousness and demands for better quality products, coupled with increased competition

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have seen environmental and social issues become an important management consideration (Mihaliˇc, 2000). Further reason for the addition of sustainable development concepts to destination competitiveness considerations is that sustainability is at the forefront of the political agendas of many countries (Berke, 2002). To ensure both the long–term sustainability of the destination and continuing market competitiveness, tourism destinations must proactively and strategically plan for future developments (Ritchie, 1999). As Chon and Olsen (1990:213) state, “today’s tourism environment is increasingly competitive and complex. . . and tourism organizations at national, state and local levels must make estimates about the what is likely to happen in the future and. . . decide how to adjust to future events”. In the 21st century the world tourism environment is not only competitive and complex it is also politically unstable and continuously changing, and in order to make better estimates of future events and sound management decisions, the strategic planning process should be formally incorporated into the structure of operations (Chon & Olsen, 1990). Similarly, Faulkner (1994:231) notes that, “in any country, the emergence and continuation of tourism as a dynamic and viable industry is dependent upon the adoption of a strategic approach to planning and marketing”. Hudson, Ritchie and Timur (2004) comment that attractive, well functioning and highly competitive destinations do not exist by chance but are the result of a well–planned environment where appropriate forms of development are encouraged and facilitated. Therefore, when planning for the current and future development of the destination, the principles of sustainable tourism must be adopted, so that only those options which satisfy the needs of all destination stakeholders including residents and visitors, as well as preserve the natural and cultural assets of the region, and are economically viable should be considered (Faulkner, 2003; Hall, 1998; Inskeep, 1991; Ritchie, 1999).

3 Sustainable Tourism Planning The sustainable approach to tourism planning stemmed from broader international concerns over ecological issues. The concept of sustainability was formally recognized by the 1987 World Commission on Environment and Development (WCED), which defined sustainable practices as those, which “meet the goals of the present without compromising the ability of future generations to meet their own needs” (WCED, 1987:43).

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Sustainable development has been advocated for the tourism sector as a possible solution to the environmental and social degradation of the industry’s resources and due to the fact that tourism is a resource industry which is dependent on nature’s endowment and society’s heritage (Cooper, 1995; Murphy, 1994). Simpson (2001) identifies two key precursors in achieving sustainable tourism destination planning which are pertinent to this discussion: the need for a more strategic and long–term orientation in tourism planning and multiple stakeholder participation in the planning process.

4 Strategic Planning The concept of strategic planning is a cornerstone of conventional management theory and has proven to be an essential prerequisite in successful and competitive organizations since the 1950s. The impetus for strategic planning in the business environment arose as cultural, economic and political events began to change the dynamics and demands of the marketplace, forcing organizations to reconsider their practices and products (Ansoff, 1979; Joyce & Woods, 1996; Macmillan & Tampoe, 2000). The solution was seen to lie in strategic planning– a rational, analytical thought process, taking into account the opportunities offered by the environment, the strengths and weaknesses of the organization, culminating in a detailed specification of both the long–term aims of the organization and the specific strategy for achieving these aims (Ansoff, 1979; Heracleous, 1998). Strategic planning has been described as “a comprehensive plan of action that sets a critical direction, and guides the allocation of resources to achieve long term objectives” (Schermerhorn, 1996:160). It can also be defined as the process of developing and maintaining a strategic fit between the organization’s goals and capabilities and its changing marketing opportunities. It relies on developing a clear company mission, supporting objectives, a sound business portfolio and coordinated functional strategies (Kotler, Adam, Brown & Armstrong, 2001). Strategic planning is used in an attempt to understand an organization’s external and internal contexts so that effective strategies can be developed to link the two and respond effectively to changes in the environment (Wortman, 1979; Bryson, 1995; David, 1999; Stokes & Wechsler, 1995). While a number of approaches have been discussed and debated (see David, 1999; Macmillan & Tampoe, 2000; Porter, 1980), an effective

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strategic planning approach is generally recognized as beginning with the identification of critical stakeholder values, using these values to articulate a broad vision for the future, establishing generic goals which will contribute to a realization of a defined vision, establishing specific objectives to bridge the gap between current status and generic goals, and assigning priorities, responsibility and control systems to monitor implementation effectiveness (Bryson, 1995; Schendel & Hofer, 1979; Simpson, 2001). Such activities are undertaken with the objective of providing an organization: an understanding of its position in the marketplace; a direction and purpose; improve communication and motivation throughout the organization; enable a proper focus on key issues such as quality, production and customers and to help the organization deliver the required results and achieve targets of profitability (Birla, 2000). Strategic planning for a tourism destination is not dissimilar to that of an organization where a sequence of choices and decisions are made about the deployment of resources to commit a destination to an agreed agenda regarding future development and management (Keane, ´ Cinn´eide & Cunningham,1996). Tourism destination strategic planO ning also is designed to be deliberate and integrative, allows for formalized higher order planning and permits the destination to adapt quickly to changing situations and develop information, planning and control systems to monitor and respond to change (Cooper, 1995). The benefit of a strategic approach for tourism destinations is that it forces destinations to look outside their artificial geographical boundaries and focus on the external environment, something which tourism destinations have not done well to date. Hall (1998) found that by doing so destinations can more readily accommodate for changing circumstances and have an increased receptiveness to opportunities in the external environment.

5 Stakeholder Participation The second identified prerequisite of a sustainable tourism planning approach identified by Simpson (2001) is the engagement of multiple stakeholders in the planning process. Tourism destinations are broad and diverse with a range of stakeholders and constituents to consider in setting a strategic direction, all of whom have legitimate interests in the future of the destination. Therefore to meet sustainability objectives,

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strategic tourism destination planning must not only be proactive, but it must also be responsive to stakeholder needs. This requires the adoption of a participatory model, involving the meaningful engagement of the community, along with industry stakeholders and relevant government agencies, in an attempt to lead to agreement on planning directions and goals (Faulkner, 2003